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• Cyber Physical Systems Security.-Risk Management for CPS Security.-Wireless Sensor Network Security for Cyber Physical Systems.-WSN Security mechanisms for CPS.- ICS/SCADA System Security for CPS.-Embedded Systems Security for Cyber Physical Systems.-Distributed Control Systems Security for CPS.-Standards for CPS.
• (source: Nielsen Book Data)

This book is a pioneering yet primary general reference resource on cyber physical systems and their security concerns. Providing a fundamental theoretical background, and a clear and comprehensive overview of security issues in the domain of cyber physical systems, it is useful for students in the fields of information technology, computer science, or computer engineering where this topic is a substantial emerging area of study. .
(source: Nielsen Book Data)

• Motivation.- Closing the Loop: Placing Ontology Usage Analysis in the Ontology Development and Deployment Lifecycle.- Ontology Usage Analysis Framework (OUSAF).- Identification Phase : Ontology Usage Network Analysis Framework (OUN-AF).- Investigation Phase: Empirical Analysis of Domain Ontology Usage (EMP-AF).
• (source: Nielsen Book Data)

This unique book succinctly summarizes the need to measure how ontologies (one of the building blocks of the Semantic Web) are currently being utilized, providing insights for various stakeholders. Where possible it improves and reuses terms in existing vocabularies/ontologies, as recommended by the Linked Data community. Recent advances in the Semantic Web have led to a proliferation of Resource Description Framework (RDF) data, which employ ontologies to semantically describe the information on the Web making it equally understandable for both humans and machines. However, to create a network effect, it is important that selective ontologies are used by more data publishers to improve the value of that ontology. For this to happen, it is vital to discover what is being used from an ontology to semantically annotate the information on the Web specific to a given domain. Answers to such basic but crucial questions can only be achieved by ascertaining how ontologies in the current semantic web are being utilized and adopted. The proposed frameworks to obtain such insights are explained with real-world examples to provide a clear and detailed description of ontology usage analysis. Both theoretical and practical, the book is of value to academics and professionals working in industry. Specifically, it is of primary interest to researchers, graduate students and practitioners in the area of the Semantic Web and its various real-world applications.
(source: Nielsen Book Data)

• Preface
• Special Topics in Pattern Recognition with Applications in Nonprofileration
• Manufacturing Feature Recognition for Mould & Die Designs: Current Status & Future Directions
• Pattern-Recognition Receptors of Oral Epithelia
• Generating-Kernel Based Nonlinear Feature Extraction Methods
• Damage Assessment Based on Pattern Recognition
• Artificial Intelligence Techniques for Assisting the Decision of Making or Postponing the Embryo Transfer
• New Perspectives on a Pattern Recognition Algorithm Based on Haken's Synergetic Computer Network- With a Comment on Artificial Intelligence & Physical Intelligence
• Active Contours for Real Time Applications
• Class Distribution Estimation in Imprecise Domains Based on Supervised Learning
• Quantitative Bioimage Analysis Using Pattern Recognition
• Advances in Mining Emerging Patterns for Supervised Classification
• On the Geometrical Aspect of Biometric Authentication
• Pattern Recognition as a New Method of Numerical Research of the Concrete Dynamic System
• Pattern Recognition from ICA Mixture Modeling
• Index.
• (source: Nielsen Book Data)

Pattern recognition involves tasks such as pattern identification, feature extraction from them and pattern classification in categories, based on the extracted features. This book presents topical research in the study of pattern recognition, including pattern recognition applications in non-proliferation; pattern recognition of machine feature identifications; pattern recognition receptors of oral epithelia; construction damage assessment based on pattern recognition; Haken's pattern recognition algorithm as applied in engineering sciences; active contours for real time applications and quantitative bioimage analysis using pattern recognition.
(source: Nielsen Book Data)

• Title page; Preface; Contents; Part I: General Purpose Applications of AI; Supervised Machine Learning: A Review of Classification Techniques; Dimension Reduction and Data Visualization Using Neural Networks; Recommender System Technologies Based on Argumentation; Knowledge Modelling Using UML Profile for Knowledge-Based Systems Development; A Semantic-Based Navigation Approach for Information Retrieval in the Semantic Web; Ontology-Based Management of Pervasive Systems; A DIYD (Do It Yourself Design) e-Commerce System for Vehicle Design Based on Ontologies and 3D Visualization.

Provides insights on how computer engineers can implement artificial intelligence (AI) in real world applications. This book presents practical applications of AI.

• Introduction, C.H. Chen
• combined neural-net/knowledge-based adaptive systems for large scale dynamic control, A.D.C. Holden and S.C. Suddarth
• a connectionist incremental expert system combining production systems and associative memory, H.F. Yin and P. Liang
• optimal hidden units for two-layer nonlinear feedforward networks, T.D. Sanger
• an incremental fine adjustment algorithm for the design of optimal interpolating networks, S.K. Sin and R.J.P. deFigueiredo
• on the asymptotic properties of recurrent neural networks for optimization, J. Wang
• a real-time image segmentation system using a connectionist classifier architecture, W.E. Blanz and S.L. Gish
• segmentation of ultrasonic images with neural network technology's on automatic active sonar classifier development, T.B. Haley
• on the relationships between statistical pattern recognition and artificial neural networks, C.H. Chen.
• (source: Nielsen Book Data)

The revitalization of neural network research in the past few years has already had a great impact on research and development in pattern recognition and artificial intelligence. Although neural network functions are not limited to pattern recognition, there is no doubt that a renewed progress in pattern recognition and its applications now critically depends on neural networks. This volume specially brings together outstanding original research papers in the area and aims to help the continued progress in pattern recognition and its applications.
(source: Nielsen Book Data)

• Chapter 1 Memristor Emulators A Note on Modeling.-
• Chapter 2 A Simple Oscillator using Memristor.-
• Chapter 3 A Hyperjerk Memristive System with Hidden Attractors.-
• Chapter 4 A Memristive System with Hidden Attractors and its Engineering Application.-
• Chapter 5 Adaptive Control, Synchronization and Circuit Simulation of a Memristor-Based.-
• Chapter 6 Modern System Design using Memristors.-
• Chapter 7 RF/Microwave Applications of Memristors.-
• Chapter 8 Theory, Modeling and Design of Memristor-Based Min-Max Circuits.-
• Chapter 9 Analysis of a 4-D Hyperchaotic Fractional-Order Memristive System with Hidden Attractors.-
• Chapter 10 Adaptive Control and Synchronization of a Memristor-Based Shinriki's System.
• (source: Nielsen Book Data)

This book reports on the latest advances in and applications of memristors, memristive devices and systems. It gathers 20 contributed chapters by subject experts, including pioneers in the field such as Leon Chua (UC Berkeley, USA) and R.S. Williams (HP Labs, USA), who are specialized in the various topics addressed in this book, and covers broad areas of memristors and memristive devices such as: memristor emulators, oscillators, chaotic and hyperchaotic memristive systems, control of memristive systems, memristor-based min-max circuits, canonic memristors, memristive-based neuromorphic applications, implementation of memristor-based chaotic oscillators, inverse memristors, linear memristor devices, delayed memristive systems, flux-controlled memristive emulators, etc. Throughout the book, special emphasis is given to papers offering practical solutions and design, modeling, and implementation insights to address current research problems in memristors, memristive devices and systems. As such, it offers a valuable reference book on memristors and memristive devices for graduate students and researchers with a basic knowledge of electrical and control systems engineering.
(source: Nielsen Book Data)

• Introduction.- Radon Transform based Automatic Posture Recognition in Ballet Dance.- Fuzzy Image Matching Based Posture Recognition in Ballet Dance.- Gesture Driven Fuzzy Interface System For Car Racing Game.- Type-2 Fuzzy Classifier based Pathological Disorder Recognition.- Probabilistic Neural Network based Dance Gesture Recognition.- Differential Evolution based Dance Composition.- EEG-Gesture based Artificial Limb Movement for Rehabilitative Applications.- Conclusions and Future Directions.- Index.
• (source: Nielsen Book Data)

This book presents a thorough analysis of gestural data extracted from raw images and/or range data with an aim to recognize the gestures conveyed by the data. It covers image morphological analysis, type-2 fuzzy logic, neural networks and evolutionary computation for classification of gestural data. The application areas include the recognition of primitive postures in ballet/classical Indian dances, detection of pathological disorders from gestural data of elderly people, controlling motion of cars in gesture-driven gaming and gesture-commanded robot control for people with neuro-motor disability. The book is unique in terms of its content, originality and lucid writing style. Primarily intended for graduate students and researchers in the field of electrical/computer engineering, the book will prove equally useful to computer hobbyists and professionals engaged in building firmware for human-computer interfaces. A prerequisite of high school level mathematics is sufficient to understand most of the chapters in the book. A basic background in image processing, although not mandatory, would be an added advantage for certain sections.
(source: Nielsen Book Data)

• Vocabularies, Schemas, Ontologies
• Requirements for and Evaluation of User Support for Large-Scale Ontology Alignment
• RODI: A Benchmark for Automatic Mapping Generation in Relational-to-Ontology Data Integration
• VocBench: a Web Application for Collaborative Development of Multilingual Thesauri
• Leveraging and Balancing Heterogeneous Sources of Evidence in Ontology Learning
• Reasoning
• A Context-Based Semantics for SPARQL Property Paths over the Web
• Distributed and Scalable OWL EL Reasoning
• Large scale rule-based Reasoning using a Laptop
• RDF Digest: Efficient Summarization of RDF/S KBs
• Linked Data
• A Comparison of Data Structures to Manage URIs on the Web of Data
• Heuristics for Fixing Common Errors in Deployed schema.org Microdata
• Semantic Web and Web Science
• LOD-based Disambiguation of Named Entities in @tweets through Context enrichment
• Knowledge Enabled Approach to Predict the Location of Twitter Users
• Semantic Data Management, Big data, Scalability
• A Compact In-Memory Dictionary for RDF data
• Quality Assessment of Linked Datasets using Probabilistic Approximations
• Cooperative Techniques for SPARQL Query Relaxation in RDF Databases
• HDT-MR: A Scalable Solution for RDF Compression with HDT and MapReduce
• Processing Aggregate Queries in a Federation of SPARQL Endpoints
• A survey of HTTP caching implementations on the open Semantic Web
• Query Execution Optimization for Clients of Triple Pattern Fragments
• Natural Language Processing and Information Retrieval LIME: the Metadata Module for OntoLex
• Learning a Cross-Lingual Semantic Representation of Relations Expressed in Text
• HAWK Hybrid Question Answering using Linked Data
• Machine Learning
• Automating RDF Dataset Transformation and Enrichment
• Semi-supervised Instance Matching Using Boosted Classifiers
• Assigning Semantic Labels to Data Sources
• Inductive Classification through Evidence-based Models and their Ensembles
• Mobile Web, Internet of Things and Semantic Streams
• Standardized and Efficient RDF Encoding for Constrained Embedded Networks
• Services, Web APIs, and the Web of Things SPSC: Efficient Composition of Semantic Services in Unstructured P2P Networks
• Data as a Service: The Semantic Web Redeployed
• Cognition and Semantic Web
• On Coherent Indented Tree Visualization of RDF Graphs
• Gagg: A Graph Aggregation Operator
• FrameBase: Representing N-ary Relations using Semantic Frames
• Human Computation and Crowdsourcing
• Towards hybrid NER: a study of content and crowdsourcing-related performance factors
• Ranking Entities in the Age of Two Webs, An Application to Semantic Snippets
• In-Use Industrial Track
• Troubleshooting and Optimizing Named Entity Resolution Systems in the Industry
• Using Ontologies For Modeling Virtual Reality Scenarios
• Supporting Open Collaboration in Science through Explicit and Linked Semantic Description of Processes
• Crowdmapping Digital Social Innovation with Linked data
• Desperately searching for travel offers? Formulate better queries with some help from Linked Data
• Towards the Linked Russian Heritage Cloud: Data enrichment and Publishing
• From Symptoms to Diseases
• Creating the Missing Link
• Using semantic web technologies for enterprise architecture analysis
• PADTUN
• Using Semantic Technologies in Tunnel Diagnosis and Maintenance Domain.

This book constitutes the refereed proceedings of the 12th Extended Semantic Web Conference, ESWC 2014, held in Anissaras, Portoroz, Slovenia, in May/June 2015. The 43 revised full papers presented together with three invited talks were carefully reviewed and selected from 164 submissions. This program was completed by a demonstration and poster session, in which researchers had the chance to present their latest results and advances in the form of live demos. In addition, the PhD Symposium program included 12 contributions, selected out of 16 submissions. The core tracks of the research conference were complemented with new tracks focusing on linking machine and human computation at web scale (cognition and Semantic Web, Human Computation and Crowdsourcing) beside the following subjects Vocabularies, Schemas, Ontologies, Reasoning, Linked Data, Semantic Web and Web Science, Semantic Data Management, Big data, Scalability, Natural Language Processing and Information Retrieval, Machine Learning, Mobile Web, Internet of Things and Semantic Streams, Services, Web APIs, and the Web of Things, Cognition and Semantic Web, Human Computation and Crowdsourcing, and In-Use Industrial Track as well.

• Introduction.- Problem Description.- Background.- Related Work.- The PLASTIC Algorithms.- Theoretical Analysis of PLASTIC.- Empirical Evaluation.- Discussion and Conclusion.
• (source: Nielsen Book Data)

This book is devoted to the encounter and interaction of agents such as robots with other agents and describes how they cooperate with their previously unknown teammates, forming an Ad Hoc team. It presents a new algorithm, PLASTIC, that allows agents to quickly adapt to new teammates by reusing knowledge learned from previous teammates. PLASTIC is instantiated in both a model-based approach, PLASTIC-Model and a policy-based approach, PLASTIC-Policy. In addition to reusing knowledge learned from previous teammates, PLASTIC also allows users to provide expert-knowledge and can use transfer learning (such as the new Two Stage Transfer algorithm) to quickly create models of new teammates when it has some information about its new teammates. The effectiveness of the algorithm is demonstrated on three domains, ranging from multi-armed bandits to simulated robot soccer games.
(source: Nielsen Book Data)

• Foreword
• 1. The Semantic Web
• 2. Web Knowledge
• 3. Reactive Agents
• 4. Practical Reasoning and Deductive Agents
• 5. Reasoning on the Web
• 6. Agent Communication
• 7. Semantic Web Services
• 8. Conclusions
• Index.
• (source: Nielsen Book Data)

This highly topical text considers the construction of the next generation of the Web, called the Semantic Web. This will enable computers to automatically consume Web-based information, overcoming the human-centric focus of the Web as it stands at present, and expediting the construction of a whole new class of knowledge-based applications that will intelligently utilise Web content. The text is structured into three main sections on knowledge representation techniques, reasoning with multi-agent systems, and knowledge services. For each of these topics, the text provides an overview of the state-of-the-art techniques and the popular standards that have been defined. Numerous small programming examples are given, which demonstrate how the benefits of the Semantic Web technologies can be realised at the present time. The main theoretical results underlying each of the technologies are presented, and the main problems and research issues which remain are summarised. Based on a course on 'Multi-Agent Systems and the Semantic Web' taught at the University of Edinburgh, this text is ideal for final-year undergraduate and graduate students in Mathematics, Computer Science, Artificial Intelligence, and Logic and researchers interested in Multi-Agent Systems and the Semantic Web.
(source: Nielsen Book Data)

• Pattern Recognition Principles.- Patterns of Go gaming by Ising model.- A Novel Criterion to Obtain the Best Feature Subset from Filter Ranking Methods.- Class-specific Reducts vs. Classic Reducts in a Rule-based Classifier: A Case Study.- On the Construction of a Specific Algebra for Composing Tonal Counterpoint.- The Impact of Basic Matrix Dimension on the Performance of Algorithms for Computing Typical Testors.- Fast Convex Hull by a Geometric Approach.- An Experimental Study on Ant Colony Optimization Hyper-heuristics for Solving the Knapsack Problem.- A Linear Time Algorithm for Computing #2SAT for Outerplanar 2-CNF Formulas.- Improving the List of Clustered Permutation on Metric Spaces for Similarity Searching on Secondary Memory.- Modelling 3-Coloring of Polygonal Trees via Incremental Satisfiability.- Deep Learning, Neural Networks and Associative Memories.- Performance Analysis of Deep Neural Networks for Classification of Gene-Expression Microarrays.- Extreme Points of Convex Polytopes Derived from Lattice Autoassociative Memories.- A Comparison of Deep Neural Network Algorithms for Recognition of EEG Motor Imagery Signals.- Learning Word and Sentence Embeddings using a Generative Convolutional Network.- Dense Captioning of Natural Scenes in Spanish.- Automated Detection of Hummingbirds in Images: a Deep Learning Approach.- Data Mining.- Patterns in Poor Learning Engagement in Students While They are Solving Mathematics Exercises in an Affective Tutoring System Related to Frustration.- Pattern Discovery in Mixed Data Bases.- Image Clustering based on Frequent Approximate Subgraph Mining.- Validation of Semantic Relation of Synonymy in Domain Ontologies using Lexico-Syntactic Patterns and Acronyms.- Computer Vision.- Scene Text Segmentation Based on Local Image Phase Information and MSER Method.- A Lightweight Library for Augmented Reality Applications.- Point Set Matching with Order Type.- Including Foreground and Background Information in Maya Hieroglyph Representation.- A Fast Algorithm for Robot Localization using Multiple Sensing Units.- Improving Breast Mass Classification through Kernel Methods and the Fusion of Clinical Data and Image Descriptors.- An Improved Stroke Width Transform to Detect Race Bib Numbers.- Scaled CCR Histogram for Scale-invariant Texture Classification.
• (source: Nielsen Book Data)

This book constitutes the proceedings of the 10th Mexican Conference on Pattern Recognition, MCPR 2018, held in Puebla, Mexico, in June 2018. The 28 papers presented in this volume were carefully reviewed and selected from 44 submissions. They were organized in topical sections named: pattern recognition principles; deep learning, neural networks and associative memories; data mining; and computer vision. .
(source: Nielsen Book Data)

• Discrete-Time Signals and Circuits Fundamentals.- Introduction to Adaptive Signal Processing.- Optimal Linear Filter Theory.- Least Squares Method.- First Order Adaptive Algorithms.- Second Order Adaptive Algorithms.- Block and Transform Domain Algorithms.- Linear Prediction and Recursive Order Algorithms.- Discrete Space-Time Filtering.- Appendix A: Linear Algebra Foundation.- Appendix B: Non Linear Programming Fundamentals.-Appendix C: Random Variables, Stochastic Processes and Estimation Theory.
• (source: Nielsen Book Data)

This book is an accessible guide to adaptive signal processing methods that equips the reader with advanced theoretical and practical tools for the study and development of circuit structures and provides robust algorithms relevant to a wide variety of application scenarios. Examples include multimodal and multimedia communications, the biological and biomedical fields, economic models, environmental sciences, acoustics, telecommunications, remote sensing, monitoring and in general, the modeling and prediction of complex physical phenomena. The reader will learn not only how to design and implement the algorithms but also how to evaluate their performance for specific applications utilizing the tools provided. While using a simple mathematical language, the employed approach is very rigorous. The text will be of value both for research purposes and for courses of study.
(source: Nielsen Book Data)

• Cognitive Radio Architecture
• Collaborative Adaptation
• Signaling Options
• Agent Communication Language
• An Example: Collaborative Link Adaptation
• Knowledge and Inference
• Cognitive Radio Ontology
• Implementation of Collaborative Link Optimization
• Evaluations.

This book provides an introduction to software-defined radio and cognitive radio, along with methodologies for applying knowledge representation, semantic web, logic reasoning and artificial intelligence to cognitive radio, enabling autonomous adaptation and flexible signaling. Readers from the wireless communications and software-defined radio communities will use this book as a reference to extend software-defined radio to cognitive radio, using the semantic technology described. Readers with a background in semantic web and artificial intelligence will find in this book the application of semantic web and artificial intelligence technologies to wireless communications. For readers in networks and network management, this book presents a new approach to enable interoperability, collaborative optimization and flexible adaptation of network components. Provides a comprehensive ontology covering the core concepts of wireless communications using a formal language; Presents the technical realization of using a formal language to exchange control messages, achieving autonomous adaptation of a communications link; Describes an architecture that enables radios to use a formal language to send inquiries and requests to other nodes, accept, interpret and execute such requests using their local policies and modify their own parameters.

• Foreword. Introduction. Part I: Measuring Success. 1 Why Understanding Your Web Traffic Is Important to Your Business. 2 Available Methodologies. 3 Where Google Analytics Fits. Part II: Using Google Analytics Reports. 4 Using the Google Analytics Interface. 5 Top 10 Reports Explained Part III: Implementing Google Analytics. 6 Getting Started. 7 Advanced Implementation. 8 Best Practices Configuration Guide. 9 Google Analytics Hacks. Part IV: Using Visitor Data to Drive Website Improvement. 10 Focusing on Key Performance Indicators. 11 Real-World Tasks. 12 Integrating Google Analytics Data with Third-Party Systems. Appendix A Regular Expression Overview. Understanding the Fundamentals. Regex Examples. Appendix B Useful Tools. Tools to Audit Your GATC Deployment. Firefox Add-ons. Desktop Helper Applications. Appendix C Recommended Further Reading. Books on Web Analytics and Related Areas. Web Resources. Blog Roll for Web Analytics. Index.
• (source: Nielsen Book Data)

Packed with insider tips and tricks, this how-to guide is fully revised to cover the latest version of Google Analytics and shows you how to implement proven Web analytics methods and concepts. This second edition of the bestselling Advanced Web Metrics with Google Analytics is the perfect book for marketers, vendors, consultants, and Webmasters who want to learn the installation, configuration, tracking techniques, and best practices of Google Analytics.* Google Analytics is a free tool that measures Web site effectiveness and helps users better understand how web site performance; this book is a detailed usage guide written by one of the software's original creators* Explains what filters keep data accurate, how to measure Flash usage and tag for e-mail marketing, and what visitor segmentation provides the most useful feedback* Examines principles and practices of Web analytics, then shows how to use GA's reports and how to track dynamic Web pages, banners, outgoing links, and contact forms* Discusses advanced setups for configuring goals and filters, how to integrate GA with third-party systems, and how to leverage the new API Advanced Web Metrics with Google Analytics, Second Edition is valuable for both novice and experienced users of Google Analytics.
(source: Nielsen Book Data)

• PREFACE. NOTATION.
• 1 Introduction. 1.1 Learning and Statistical Estimation. 1.2 Statistical Dependency and Causality. 1.3 Characterization of Variables. 1.4 Characterization of Uncertainty. 1.5 Predictive Learning versus Other Data Analytical Methodologies.
• 2 Problem Statement, Classical Approaches, and Adaptive Learning. 2.1 Formulation of the Learning Problem. 2.1.1 Objective of Learning. 2.1.2 Common Learning Tasks. 2.1.3 Scope of the Learning Problem Formulation. 2.2 Classical Approaches. 2.2.1 Density Estimation. 2.2.2 Classification. 2.2.3 Regression. 2.2.4 Solving Problems with Finite Data. 2.2.5 Nonparametric Methods. 2.2.6 Stochastic Approximation. 2.3 Adaptive Learning: Concepts and Inductive Principles. 2.3.1 Philosophy, Major Concepts, and Issues. 2.3.2 A Priori Knowledge and Model Complexity. 2.3.3 Inductive Principles. 2.3.4 Alternative Learning Formulations. 2.4 Summary.
• 3 Regularization Framework. 3.1 Curse and Complexity of Dimensionality. 3.2 Function Approximation and Characterization of Complexity. 3.3 Penalization. 3.3.1 Parametric Penalties. 3.3.2 Nonparametric Penalties. 3.4 Model Selection (Complexity Control). 3.4.1 Analytical Model Selection Criteria. 3.4.2 Model Selection via Resampling. 3.4.3 Bias-Variance Tradeoff. 3.4.4 Example of Model Selection. 3.4.5 Function Approximation versus Predictive Learning. 3.5 Summary.
• 4 Statistical Learning Theory. 4.1 Conditions for Consistency and Convergence of ERM. 4.2 Growth Function and VC Dimension. 4.2.1 VC Dimension for Classification and Regression Problems. 4.2.2 Examples of Calculating VC Dimension. 4.3 Bounds on the Generalization. 4.3.1 Classification. 4.3.2 Regression. 4.3.3 Generalization Bounds and Sampling Theorem. 4.4 Structural Risk Minimization. 4.4.1 Dictionary Representation. 4.4.2 Feature Selection. 4.4.3 Penalization Formulation. 4.4.4 Input Preprocessing. 4.4.5 Initial Conditions for Training Algorithm. 4.5 Comparisons of Model Selection for Regression. 4.5.1 Model Selection for Linear Estimators. 4.5.2 Model Selection for k-Nearest-Neighbor Regression. 4.5.3 Model Selection for Linear Subset Regression. 4.5.4 Discussion. 4.6 Measuring the VC Dimension. 4.7 VC Dimension, Occam's Razor, and Popper's Falsifiability. 4.8 Summary and Discussion.
• 5 Nonlinear Optimization Strategies. 5.1 Stochastic Approximation Methods. 5.1.1 Linear Parameter Estimation. 5.1.2 Backpropagation Training of MLP Networks. 5.2 Iterative Methods. 5.2.1 EM Methods for Density Estimation. 5.2.2 Generalized Inverse Training of MLP Networks. 5.3 Greedy Optimization. 5.3.1 Neural Network Construction Algorithms. 5.3.2 Classification and Regression Trees. 5.4 Feature Selection, Optimization, and Statistical Learning Theory. 5.5 Summary.
• 6 Methods for Data Reduction and Dimensionality Reduction. 6.1 Vector Quantization and Clustering. 6.1.1 Optimal Source Coding in Vector Quantization. 6.1.2 Generalized Lloyd Algorithm. 6.1.3 Clustering. 6.1.4 EM Algorithm for VQ and Clustering. 6.1.5 Fuzzy Clustering. 6.2 Dimensionality Reduction: Statistical Methods. 6.2.1 Linear Principal Components. 6.2.2 Principal Curves and Surfaces. 6.2.3 Multidimensional Scaling. 6.3 Dimensionality Reduction: Neural Network Methods. 6.3.1 Discrete Principal Curves and Self-Organizing Map Algorithm. 6.3.2 Statistical Interpretation of the SOM Method. 6.3.3 Flow-Through Version of the SOM and Learning Rate Schedules. 6.3.4 SOM Applications and Modifications. 6.3.5 Self-Supervised MLP. 6.4 Methods for Multivariate Data Analysis. 6.4.1 Factor Analysis. 6.4.2 Independent Component Analysis. 6.5 Summary.
• 7 Methods for Regression. 7.1 Taxonomy: Dictionary versus Kernel Representation. 7.2 Linear Estimators. 7.2.1 Estimation of Linear Models and Equivalence of Representations. 7.2.2 Analytic Form of Cross-Validation. 7.2.3 Estimating Complexity of Penalized Linear Models. 7.2.4 Nonadaptive Methods. 7.3 Adaptive Dictionary Methods. 7.3.1 Additive Methods and Projection Pursuit Regression. 7.3.2 Multilayer Perceptrons and Backpropagation. 7.3.3 Multivariate Adaptive Regression Splines. 7.3.4 Orthogonal Basis Functions and Wavelet Signal Denoising. 7.4 Adaptive Kernel Methods and Local Risk Minimization. 7.4.1 Generalized Memory-Based Learning. 7.4.2 Constrained Topological Mapping. 7.5 Empirical Studies. 7.5.1 Predicting Net Asset Value (NAV) of Mutual Funds. 7.5.2 Comparison of Adaptive Methods for Regression. 7.6 Combining Predictive Models. 7.7 Summary.
• 8 Classification. 8.1 Statistical Learning Theory Formulation. 8.2 Classical Formulation. 8.2.1 Statistical Decision Theory. 8.2.2 Fisher's Linear Discriminant Analysis. 8.3 Methods for Classification. 8.3.1 Regression-Based Methods. 8.3.2 Tree-Based Methods. 8.3.3 Nearest-Neighbor and Prototype Methods. 8.3.4 Empirical Comparisons. 8.4 Combining Methods and Boosting. 8.4.1 Boosting as an Additive Model. 8.4.2 Boosting for Regression Problems. 8.5 Summary.
• 9 Support Vector Machines. 9.1 Motivation for Margin-Based Loss. 9.2 Margin-Based Loss, Robustness, and Complexity Control. 9.3 Optimal Separating Hyperplane. 9.4 High-Dimensional Mapping and Inner Product Kernels. 9.5 Support Vector Machine for Classification. 9.6 Support Vector Implementations. 9.7 Support Vector Regression. 9.8 SVM Model Selection. 9.9 Support Vector Machines and Regularization. 9.10 Single-Class SVM and Novelty Detection. 9.11 Summary and Discussion.
• 10 Noninductive Inference and Alternative Learning Formulations. 10.1 Sparse High-Dimensional Data. 10.2 Transduction. 10.3 Inference Through Contradictions. 10.4 Multiple-Model Estimation. 10.5 Summary.
• 11 Concluding Remarks. Appendix A: Review of Nonlinear Optimization. Appendix B: Eigenvalues and Singular Value Decomposition. References. Index.
• (source: Nielsen Book Data)

An interdisciplinary framework for learning methodologies-covering statistics, neural networks, and fuzzy logic, this book provides a unified treatment of the principles and methods for learning dependencies from data. It establishes a general conceptual framework in which various learning methods from statistics, neural networks, and fuzzy logic can be applied-showing that a few fundamental principles underlie most new methods being proposed today in statistics, engineering, and computer science. Complete with over one hundred illustrations, case studies, and examples making this an invaluable text.
(source: Nielsen Book Data)

• PREFACE. NOTATION.
• 1 Introduction. 1.1 Learning and Statistical Estimation. 1.2 Statistical Dependency and Causality. 1.3 Characterization of Variables. 1.4 Characterization of Uncertainty. 1.5 Predictive Learning versus Other Data Analytical Methodologies.
• 2 Problem Statement, Classical Approaches, and Adaptive Learning. 2.1 Formulation of the Learning Problem. 2.1.1 Objective of Learning. 2.1.2 Common Learning Tasks. 2.1.3 Scope of the Learning Problem Formulation. 2.2 Classical Approaches. 2.2.1 Density Estimation. 2.2.2 Classification. 2.2.3 Regression. 2.2.4 Solving Problems with Finite Data. 2.2.5 Nonparametric Methods. 2.2.6 Stochastic Approximation. 2.3 Adaptive Learning: Concepts and Inductive Principles. 2.3.1 Philosophy, Major Concepts, and Issues. 2.3.2 A Priori Knowledge and Model Complexity. 2.3.3 Inductive Principles. 2.3.4 Alternative Learning Formulations. 2.4 Summary.
• 3 Regularization Framework. 3.1 Curse and Complexity of Dimensionality. 3.2 Function Approximation and Characterization of Complexity. 3.3 Penalization. 3.3.1 Parametric Penalties. 3.3.2 Nonparametric Penalties. 3.4 Model Selection (Complexity Control). 3.4.1 Analytical Model Selection Criteria. 3.4.2 Model Selection via Resampling. 3.4.3 Bias-Variance Tradeoff. 3.4.4 Example of Model Selection. 3.4.5 Function Approximation versus Predictive Learning. 3.5 Summary.
• 4 Statistical Learning Theory. 4.1 Conditions for Consistency and Convergence of ERM. 4.2 Growth Function and VC Dimension. 4.2.1 VC Dimension for Classification and Regression Problems. 4.2.2 Examples of Calculating VC Dimension. 4.3 Bounds on the Generalization. 4.3.1 Classification. 4.3.2 Regression. 4.3.3 Generalization Bounds and Sampling Theorem. 4.4 Structural Risk Minimization. 4.4.1 Dictionary Representation. 4.4.2 Feature Selection. 4.4.3 Penalization Formulation. 4.4.4 Input Preprocessing. 4.4.5 Initial Conditions for Training Algorithm. 4.5 Comparisons of Model Selection for Regression. 4.5.1 Model Selection for Linear Estimators. 4.5.2 Model Selection for k-Nearest-Neighbor Regression. 4.5.3 Model Selection for Linear Subset Regression. 4.5.4 Discussion. 4.6 Measuring the VC Dimension. 4.7 VC Dimension, Occam's Razor, and Popper's Falsifiability. 4.8 Summary and Discussion.
• 5 Nonlinear Optimization Strategies. 5.1 Stochastic Approximation Methods. 5.1.1 Linear Parameter Estimation. 5.1.2 Backpropagation Training of MLP Networks. 5.2 Iterative Methods. 5.2.1 EM Methods for Density Estimation. 5.2.2 Generalized Inverse Training of MLP Networks. 5.3 Greedy Optimization. 5.3.1 Neural Network Construction Algorithms. 5.3.2 Classification and Regression Trees. 5.4 Feature Selection, Optimization, and Statistical Learning Theory. 5.5 Summary.
• 6 Methods for Data Reduction and Dimensionality Reduction. 6.1 Vector Quantization and Clustering. 6.1.1 Optimal Source Coding in Vector Quantization. 6.1.2 Generalized Lloyd Algorithm. 6.1.3 Clustering. 6.1.4 EM Algorithm for VQ and Clustering. 6.1.5 Fuzzy Clustering. 6.2 Dimensionality Reduction: Statistical Methods. 6.2.1 Linear Principal Components. 6.2.2 Principal Curves and Surfaces. 6.2.3 Multidimensional Scaling. 6.3 Dimensionality Reduction: Neural Network Methods. 6.3.1 Discrete Principal Curves and Self-Organizing Map Algorithm. 6.3.2 Statistical Interpretation of the SOM Method. 6.3.3 Flow-Through Version of the SOM and Learning Rate Schedules. 6.3.4 SOM Applications and Modifications. 6.3.5 Self-Supervised MLP. 6.4 Methods for Multivariate Data Analysis. 6.4.1 Factor Analysis. 6.4.2 Independent Component Analysis. 6.5 Summary.
• 7 Methods for Regression. 7.1 Taxonomy: Dictionary versus Kernel Representation. 7.2 Linear Estimators. 7.2.1 Estimation of Linear Models and Equivalence of Representations. 7.2.2 Analytic Form of Cross-Validation. 7.2.3 Estimating Complexity of Penalized Linear Models. 7.2.4 Nonadaptive Methods. 7.3 Adaptive Dictionary Methods. 7.3.1 Additive Methods and Projection Pursuit Regression. 7.3.2 Multilayer Perceptrons and Backpropagation. 7.3.3 Multivariate Adaptive Regression Splines. 7.3.4 Orthogonal Basis Functions and Wavelet Signal Denoising. 7.4 Adaptive Kernel Methods and Local Risk Minimization. 7.4.1 Generalized Memory-Based Learning. 7.4.2 Constrained Topological Mapping. 7.5 Empirical Studies. 7.5.1 Predicting Net Asset Value (NAV) of Mutual Funds. 7.5.2 Comparison of Adaptive Methods for Regression. 7.6 Combining Predictive Models. 7.7 Summary.
• 8 Classification. 8.1 Statistical Learning Theory Formulation. 8.2 Classical Formulation. 8.2.1 Statistical Decision Theory. 8.2.2 Fisher's Linear Discriminant Analysis. 8.3 Methods for Classification. 8.3.1 Regression-Based Methods. 8.3.2 Tree-Based Methods. 8.3.3 Nearest-Neighbor and Prototype Methods. 8.3.4 Empirical Comparisons. 8.4 Combining Methods and Boosting. 8.4.1 Boosting as an Additive Model. 8.4.2 Boosting for Regression Problems. 8.5 Summary.
• 9 Support Vector Machines. 9.1 Motivation for Margin-Based Loss. 9.2 Margin-Based Loss, Robustness, and Complexity Control. 9.3 Optimal Separating Hyperplane. 9.4 High-Dimensional Mapping and Inner Product Kernels. 9.5 Support Vector Machine for Classification. 9.6 Support Vector Implementations. 9.7 Support Vector Regression. 9.8 SVM Model Selection. 9.9 Support Vector Machines and Regularization. 9.10 Single-Class SVM and Novelty Detection. 9.11 Summary and Discussion.
• 10 Noninductive Inference and Alternative Learning Formulations. 10.1 Sparse High-Dimensional Data. 10.2 Transduction. 10.3 Inference Through Contradictions. 10.4 Multiple-Model Estimation. 10.5 Summary.
• 11 Concluding Remarks. Appendix A: Review of Nonlinear Optimization. Appendix B: Eigenvalues and Singular Value Decomposition. References. Index.
• (source: Nielsen Book Data)

An interdisciplinary framework for learning methodologies-covering statistics, neural networks, and fuzzy logic, this book provides a unified treatment of the principles and methods for learning dependencies from data. It establishes a general conceptual framework in which various learning methods from statistics, neural networks, and fuzzy logic can be applied-showing that a few fundamental principles underlie most new methods being proposed today in statistics, engineering, and computer science. Complete with over one hundred illustrations, case studies, and examples making this an invaluable text.
(source: Nielsen Book Data)

• Theoretical computer science.- Artificial intelligence.- Microeconomics.- Problems at the intersection of computation, game theory and economics. .
• (source: Nielsen Book Data)

This book constitutes the thoroughly refereed proceedings of the 13th International Conference on Web and Internet Economics, WINE 2017, held in Bangalore, India, in December 2017. The 28 full and 6 short papers presented were carefully reviewed and selected from 89 submissions. The papers reflect the work of researchers in theoretical computer science, artificial intelligence, and microeconomics who have joined forces to tackle problems at the intersection of computation, game theory and economics. .
(source: Nielsen Book Data)

• Chapter 1. Introduction.-
• Chapter 2. Sparse Linear Model.-
• Chapter 3. Blind Demixing.-
• Chapter 4. Sparse Blind Demixing.-
• Chapter 5. Shuffled Linear Regression.-
• Chapter 6. Learning Augmented Methods.-
• Chapter 7. Conclusions and Discussions.-
• Chapter 8. Appendix. .
• (source: Nielsen Book Data)

• 4.5.1 Optimization on Product Manifolds
• 4.5.2 Smoothed Riemannian Optimization
• 4.5.3 Simulation Results
• 4.6 Summary
• References
• 5 Shuffled Linear Regression
• 5.1 Joint Data Decoding and Device Identification
• 5.2 Problem Formulation
• 5.3 Maximum Likelihood Estimation Based Approaches
• 5.3.1 Sorting Based Algorithms
• 5.3.2 Approximation Algorithm
• 5.4 Algebraic-Geometric Approach
• 5.4.1 Eliminating Π via Symmetric Polynomials
• 5.4.2 Theoretical Analysis
• 5.4.2.1 Exact Data
• 5.4.2.2 Corrupted Data
• 5.4.3 Algebraically Initialized Expectation-Maximization

The recent developments in wireless communications, networking, and embedded systems have driven various innovative Internet of Things (IoT) applications, e.g., smart cities, mobile healthcare, autonomous driving and drones. A common feature of these applications is the stringent requirements for low-latency communications. Considering the typical small payload size of IoT applications, it is of critical importance to reduce the size of the overhead message, e.g., identification information, pilot symbols for channel estimation, and control data. Such low-overhead communications also help to improve the energy efficiency of IoT devices. Recently, structured signal processing techniques have been introduced and developed to reduce the overheads for key design problems in IoT networks, such as channel estimation, device identification, and message decoding. By utilizing underlying system structures, including sparsity and low rank, these methods can achieve significant performance gains. This book provides an overview of four general structured signal processing models: a sparse linear model, a blind demixing model, a sparse blind demixing model, and a shuffled linear model, and discusses their applications in enabling low-overhead communications in IoT networks. Further, it presents practical algorithms based on both convex and nonconvex optimization approaches, as well as theoretical analyses that use various mathematical tools.
(source: Nielsen Book Data)

• Introduction.- Background and Literature Survey.- VLSI Architecture for Fast Three Step Search Algorithm.- Parallel Architecture for Successive Elimination Block Matching Algorithm.- Fast One-Bit Transformation Architectures.- Efficient Pixel Truncation Algorithm and Architecture.- Introduction to Scalable Image and Video Coding.- Forward Plans.- 8 Forward Plans.- A Matlab Programs.- B Verilog Modules.
• (source: Nielsen Book Data)

The need of video compression in the modern age of visual communication cannot be over-emphasized. This monograph will provide useful information to the postgraduate students and researchers who wish to work in the domain of VLSI design for video processing applications. In this book, one can find an in-depth discussion of several motion estimation algorithms and their VLSI implementation as conceived and developed by the authors. It records an account of research done involving fast three step search, successive elimination, one-bit transformation and its effective combination with diamond search and dynamic pixel truncation techniques. Two appendices provide a number of instances of proof of concept through Matlab and Verilog program segments. In this aspect, the book can be considered as first of its kind. The architectures have been developed with an eye to their applicability in everyday low-power handheld appliances including video camcorders and smartphones.
(source: Nielsen Book Data)

• Introduction Motivation Shape recognition Proposed method Objectives Assumptions and input data Book organization
• Preliminary knowledge Statistics Probability Random variable Expected value Variance and deviation Covariance and correlation Moment-generating function Fourier transform Structural and syntactic pattern recognition Introduction Grammar-based passing method Graph-based matching methods Neural network Architecture Learning process Summary
• Review of shape recognition techniques 2D shape recognition Shape representation Shape recognition approaches Logo recognition Statistical approach Syntactic/structural approach Neural network Hybrid approach Polygonal approximation Indexing Matching Distance measure Hausdorff distance Summary
• System overview Preprocessing Polygonal approximation Indexing Matching
• Polygonal approximation Feature point detection overview Dynamic two-strip algorithm The proposed method Results Comparison with other methods Summary
• Logo indexing Normalization Indexing Reference angle indexing (filter 1) Line orientation indexing (filters 2 and 3) Experimental results Summary
• Logo matching Hausdorff distance Modified LHD (MLHD) Experimental results Matching results Degradation analysis Results analysis with respect to the LHD and the MHD Discussion and comparison with other methods Summary
• Applications Mobile visual search with GetFugu Using logo recognition for anti-phishing and Internet brand monitoring The LogoTrace library Real-time vehicle logo recognition Summary
• Conclusion Book summary Contribution Future work Book conclusion References
• Appendix Test images Appendix Results of feature point detection
• Index.
• (source: Nielsen Book Data)

Used by companies, organizations, and even individuals to promote recognition of their brand, logos can also act as a valuable means of identifying the source of a document. E-business applications can retrieve and catalog products according to their logos. Governmental agencies can easily inspect goods using smart mobile devices that use logo recognition techniques. However, because logos are two-dimensional shapes of varying complexity, the recognition process can be challenging. Although promising results have been found for clean logos, they have not been as robust for noisy logos. Logo Recognition: Theory and Practice is the first book to focus on logo recognition, especially under noisy conditions. Beginning with an introduction to fundamental concepts and methods in pattern and shape recognition, it surveys advances in logo recognition. The authors also propose a new logo recognition system that can be used under adverse conditions such as broken lines, added noise, and occlusion. The proposed system introduces a novel polygonal approximation, a robust indexing scheme, and a new line segment Hausdorff distance (LHD) matching method that can handle more distortion and transformation types than previous techniques. In the first stage, raw logos are transformed into normalized line segment maps. In the second stage, effective line pattern features are used to index the database to generate a moderate number of likely models. In the third stage, an improved LHD measure screens and generates the best matches. A comprehensive overview of logo recognition, the book also presents successful applications of the technology and suggests directions for future research.
(source: Nielsen Book Data)

• Low-level preprocessing, surveillance systems
• Tracking, robotics
• Identification, detection, recognition
• Behavior, activities, crowd analysis.

This book constitutes the refereed proceedings of the 4th Chinese Conference, IVS 2016, held in Beijing, China, in October 2016. The 19 revised full papers presented were carefully reviewed and selected from 45 submissions. The papers are organized in topical sections on low-level preprocessing, surveillance systems; tracking, robotics; identification, detection, recognition; behavior, activities, crowd analysis.

• Multilevel Analysis of Attributed Graphs for Explicit Graph Embedding in Vector Spaces / Muhammad Muzzamil Luqman, Jean-Yves Ramel and Josep Lladós
• Feature Grouping and Selection Over an Undirected Graph / Sen Yang, Lei Yuan, Ying-Cheng Lai, Xiaotong Shen and Peter Wonka, et al.
• Median Graph Computation by Means of Graph Embedding into Vector Spaces / Miquel Ferrer, Itziar Bardají, Ernest Valveny, Dimosthenis Karatzas and Horst Bunke
• Patch Alignment for Graph Embedding / Yong Luo, Dacheng Tao and Chao Xu
• Improving Classifications Through Graph Embeddings / Anirban Chatterjee, Sanjukta Bhowmick and Padma Raghavan
• Learning with ℓ1-Graph for High Dimensional Data Analysis / Jianchao Yang, Bin Cheng, Shuicheng Yan, Yun Fu and Thomas Huang
• Graph-Embedding Discriminant Analysis on Riemannian Manifolds for Visual Recognition / Sareh Shirazi, Azadeh Alavi, Mehrtash T. Harandi and Brian C. Lovell
• A Flexible and Effective Linearization Method for Subspace Learning / Feiping Nie, Dong Xu, Ivor W. Tsang and Changshui Zhang
• A Multi-graph Spectral Framework for Mining Multi-source Anomalies / Jing Gao, Nan Du, Wei Fan, Deepak Turaga and Srinivasan Parthasarathy, et al.
• Graph Embedding for Speaker Recognition / Z.N. Karam and W.M. Campbell.₉

Graph Embedding for Pattern Analysis covers theory methods, computation, and applications widely used in statistics, machine learning, image processing, and computer vision. This book presents the latest advances in graph embedding theories, such as nonlinear manifold graph, linearization method, graph based subspace analysis, L1 graph, hypergraph, undirected graph, and graph in vector spaces. Real-world applications of these theories are spanned broadly in dimensionality reduction, subspace learning, manifold learning, clustering, classification, and feature selection. A selective group of experts contribute to different chapters of this book which provides a comprehensive perspective of this field.

• Real time Implementation of Articial Neural Network in FPGA Platform.- Design of a 0,18um CMOS Inductor - based Bandpass Microwave Active Filter.- Improvement of the Authentication on in-Vehicle Controller Area Networks.- Comparative Evaluation of Speech Recognition systems Based on Different Toolkits.- Stability Analysis and H Performance for Nonlinear Fuzzy Networked Control Systems with Time-Varying Delay.- Driver Behavior assistance in Road Intersections.- Performance of CE-OFDM System over AWGN and Frequency-Selective Channels.- Towards a Dynamical Adaptive Finite Volume Core for Urban Flow Simulations.- An Efficiency Study of Adaptive Median Filtering for Image Denoising, Based on A Hardware Implementation.- Processing Time and Computing Resources Optimization in a Mobile Edge Computing Node.- Viscous Dissipation and Heat Source/Sink Effects on Convection Heat Transfer in a Saturated Porous Medium.- Fast and Efficient Decoding Algorithm Developed From Concatenation between A Symbol-By-Symbol Decoder and A Decoder Based On Syndrome Computing and Hash Techniques.- SWIMAC: An Ultra-Low Power Consumption MAC Protocol for IR-UWB based WSN.- An Optimal Design of a Short-Channel RF Low Noise Amplifier Using a Swarm Intelligence Techniques.
• (source: Nielsen Book Data)

• 4 Conclusion
• References
• Design of a 0.18 μm CMOS Inductor-Based Bandpass Microwave Active Filter
• 1 Introduction
• 2 Synthesis of a Grounded CMOS Active Inductor
• 3 Procedure of Designing Chebyshev Microwave Bandpass Filters
• 4 Example of a Third-Order Chebyshev Bandpass Filter
• 5 Conclusion
• References
• Improvement of the Authentication on In-Vehicle Controller Area Networks
• 1 Introduction
• 2 Development of an Authentication Method
• 2.1 Principles
• 2.2 Current Technique
• 2.3 Proposed Improvements
• 3 Hash Algorithm
• 4 Conclusion
• References

This book gathers selected research papers presented at the First International Conference on Embedded Systems and Artificial Intelligence (ESAI 2019), held at Sidi Mohamed Ben Abdellah University, Fez, Morocco, on 2-3 May 2019. Highlighting the latest innovations in Computer Science, Artificial Intelligence, Information Technologies, and Embedded Systems, the respective papers will encourage and inspire researchers, industry professionals, and policymakers to put these methods into practice.
(source: Nielsen Book Data)

• Pattern recognition algorithms.- Reproducible research frameworks.- Reproducible research results.- Previous works on reproducible research.
• (source: Nielsen Book Data)

This book constitutes the thoroughly refereed post-proceedings of the First International Workshop on Reproducible Research in Pattern Recognition, RRPR 2016, held in Cancun, Mexico, in December 2016. The 12 revised full papers, among them 2 invited talks, presented were carefully reviewed and selected from 16 submissions. They focus on pattern recognition algorithms; reproducible research frameworks; reproducible research results, previous works on reproducible research.
(source: Nielsen Book Data)

• Features, Learning, and Classifiers. Toward Computer-Aided Interpretation of Situations / Juliusz L. Kulikowski
• Classification of Multi-dimensional Distributions Using Order Statistics Criteria / A. Thomas, B. John Oommen
• Knowledge Extraction from Graph-Based Structures in Conceptual Design / Grażyna Ślusarczyk
• Estimation of the Relations of: Equivalence, Tolerance and Preference on the Basis of Pairwise Comparisons / Leszek Klukowski
• Generalized Constraint Design of Linear-Phase FIR Digital Filters / Norbert Henzel, Jacek M. Leski
• Reduced Kernel Extreme Learning Machine / Wanyu Deng, Qinghua Zheng, Kai Zhang
• Using Positional Information in Modeling Inflorescence Discs / Malgorzata Prolejko
• A New Method for Random Initialization of the EM Algorithm for Multivariate Gaussian Mixture Learning / Wojciech Kwedlo
• Heuristic Optimization Algorithms for a Tree-Based Image Dissimilarity Measure / Bartłomiej Zieliński, Marcin Iwanowski.

• Model of Syntactic Recognition of Distorted String Patterns with the Help of GDPLL(k)-Based Automata / Janusz Jurek, Tomasz Peszek
• Decision Rules in Simple Decision Systems over Ontological Graphs / Krzysztof Pancerz
• Nonlinear Extension of the IRLS Classifier Using Clustering with Pairs of Prototypes / Michal Jezewski, Jacek M. Leski
• Separable Linearization of Learning Sets by Ranked Layer of Radial Binary Classifiers / Leon Bobrowski, Magdalena Topczewska
• Hidden Markov Models For Two-Dimensional Data / Janusz Bobulski
• Methods of Learning Classifier Competence Applied to the Dynamic Ensemble Selection / Maciej Krysmann, Marek Kurzynski
• The Method of Improving the Structure of the Decision Tree Given by the Experts / Robert Burduk
• Biometrics. Face Detection and Recognition under Heterogeneous Database Based on Fusion of Catadioptric and PTZ Vision Sensors / Aditya Raj [and others]
• Eigenfaces, Fisherfaces, Laplacianfaces, Marginfaces -- How to Face the Face Verification Task / Maciej Smiatacz.

• A Content Based Feature Combination Method for Face Recognition / Madeena Sultana, Marina Gavrilova
• Face Recognition Based on Sequence of Images / Jacek Komorowski, Przemyslaw Rokita
• Statistical Analysis in Signature Recognition System Based on Levenshtein Distance / Malgorzata Palys, Rafal Doroz, Piotr Porwik
• A Computational Assessment of a Blood Vessel's Roughness / Tomasz Wesolowski, Krzysztof Wrobel
• Performance Benchmarking of Different Binarization Techniques for Fingerprint-Based Biometric Authentication / Soharab Hossain Shaikh, Khalid Saeed, Nabendu Chaki
• Biometric Features Selection with k-Nearest Neighbours Technique and Hotelling Adaptation Method / Piotr Porwik, Rafal Doroz
• Data Stream Classification and Big Data Analytics. Predictive Regional Trees to Supplement Geo-Physical Random Fields / Annalisa Appice, Sonja Pravilovic, Donato Malerba
• Extending Bagging for Imbalanced Data / Jerzy Błaszczyński, Jerzy Stefanowski, Łukasz Idkowiak.

• Rule Chains for Visualizing Evolving Fuzzy Rule-Based Systems / Sascha Henzgen, Marc Strickert, Eyke Hüllermeier
• Recovery Analysis for Adaptive Learning from Non-stationary Data Streams / Ammar Shaker, Eyke Hüllermeier
• Analysis of Roles and Groups in Blogosphere / Bogdan Gliwa, Anna Zygmunt, Jarosław Koźlak
• Knowledge Generalization from Long Sequence of Execution Scenarios / Radosław Z. Ziembiński
• Incremental Learning and Forgetting in One-Class Classifiers for Data Streams / Bartosz Krawczyk, Michał Woźniak
• Comparable Study of Statistical Tests for Virtual Concept Drift Detection / Piotr Sobolewski, Michał Woźniak
• Image Processing and Computer Vision. An Experimental Comparison of Fourier-Based Shape Descriptors in the General Shape Analysis Problem / Katarzyna Gościewska, Dariusz Frejlichowski
• Extraction of the Foreground Regions by Means of the Adaptive Background Modelling Based on Various Colour Components for a Visual Surveillance System / Dariusz Frejlichowski [and others].

• Repeatability Measurements for 2D Interest Point Detectors on 3D Models / Simon R. Lang, Martin H. Luerssen, David M.W. Powers
• Extended Investigations on Skeleton Graph Matching for Object Recognition / Jens Hedrich [and others]
• Low-Level Image Features for Stamps Detection and Classification / Paweł Forczmański, Andrzej Markiewicz
• Stochastic Approximation to Reconstruction of Vector-Valued Images / Dariusz Borkowski
• Image Segmentation with Use of Cross-Entropy Clustering / Marek Śmieja, Jacek Tabor
• Detection of Disk-Like Particles in Electron Microscopy Images / P. Spurek, J. Tabor, E. Zając
• A GPU Accelerated Local Polynomial Approximation Algorithm for Efficient Denoising of MR Images / Artur Klepaczko
• Altair: Automatic Image Analyzer to Assess Retinal Vessel Caliber / Gabino Verde [and others]
• Real-Time Wrist Localization in Hand Silhouettes / Tomasz Grzejszczak, Jakub Nalepa, Michal Kawulok
• An s-layered Grade Decomposition of Images / Maria Grzegorek.

• System-Level Hardware Implementation of Simplified Low-Level Color Image Descriptor / Paweł Forczmański, Piotr Dziurzański
• Reconstruction of Head Surface Model from Single Scan / Krzysztof Skabek, Dawid Łapczynski
• The Effectiveness of Matching Methods for Rectified Images / Pawel Popielski, Zygmunt Wrobel, Robert Koprowski
• The Print-Scan Problem in Printed Steganography of Face Images / Włodzimierz Kasprzak, Maciej Stefańczyk, Jan Popiołkiewicz
• Phototool Geometry Verification / Jarosław Zdrojewski, Adam Marchewka
• Structure from Motion in Three -- Dimensional Modeling of Human Head / Anna Wójcicka, Zygmunt Wróbel
• A Short Overview of Feature Extractors for Knuckle Biometrics / Michał Choraś
• Three-Stage Method of Text Region Extraction from Diagram Raster Images / Jerzy Sas, Andrzej Zolnierek
• Medical Applications. Time Series of Fuzzy Sets in Classification of Electrocardiographic Signals / Jacek M. Leski, Norbert Henzel.

• Interpolation Procedure in Filtered Backprojection Algorithm for the Limited-Angle Tomography / Aleksander Denisiuk
• Classification of Uterine Electrical Activity Patterns for Early Detection of Preterm Birth / Janusz Jezewski [and others]
• Diagnosis of Bipolar Disorder Based on Principal Component Analysis and SVM / M. Termenon [and others]
• Genetic Algorithms in EEG Feature Selection for the Classification of Movements of the Left and Right Hand / Izabela Rejer
• On the Use of Programmed Automata for a Verification of ECG Diagnoses / Mariusz Flasiński, Piotr Flasiński, Ewa Konduracka
• Blood Flow Modeling in a Synthetic Cylindrical Vessel for Validating Methods of Vessel Segmentation in MRA Images / Grzegorz Dwojakowski, Artur Klepaczko, Andrzej Materka
• Swarm Optimization and Multi-level Thresholding of Cytological Images for Breast Cancer Diagnosis / Marek Kowal [and others]
• Detecting Overlapped Nuclei Regions in the Feulgen-Stained Cytological Smears / Bogusław D. Piętka, Annamonika Dulewicz.

• Density Invariant Detection of Osteoporosis Using Growing Neural Gas / Igor T. Podolak, Stanisław K. Jastrzębski
• Cost Sensitive Hierarchical Classifiers for Non-invasive Recognition of Liver Fibrosis Stage / Bartosz Krawczyk [and others]
• Miscellaneous Applications. A Blinking Measurement Method for Driver Drowsiness Detection / Belhassen Akrout, Walid Mahdi
• Description of Human Activity Using Behavioral Primitives / Piotr Augustyniak
• How to Become Famous? Motives in Scientific Social Networks / Adam Matusiak, Mikołaj Morzy
• AdaBoost for Parking Lot Occupation Detection / Radovan Fusek [and others]
• Blink Detection Based on the Weighted Gradient Descriptor / Krystian Radlak, Bogdan Smolka
• Touchless Input Interface for Disabled / Adam Nowosielski, Łukasz Chodyła
• Architecture of the Semantically Enhanced Intellectual Property Protection System / Dariusz Ceglarek
• Validation of Clustering Techniques for User Group Modeling / Danuta Zakrzewska.

• Parking Lot Occupancy Detection Using Computational Fluid Dynamics / Tomas Fabian
• Human Fall Detection Using Kinect Sensor / Michal Kepski, Bogdan Kwolek
• Evaluation of Various Techniques for SQL Injection Attack Detection / Michał Choraś, Rafał Kozik
• Task Allocation in Distributed Mesh-Connected Machine Learning System: Simplified Busy List Algorithm with Q-Learning Based Queuing / Agnieszka Majkowska, Dawid Zydek, Leszek Koszałka
• Power Saving Algorithms for Mobile Networks Using Classifiers Ensemble / Rafal Lysiak, Marek Kurzynski
• Hardware Implementation of Fourier Transform for Real Time EMG Signals Recognition / Andrzej R. Wolczowski [and others]
• Data Preprocessing with GPU for DBSCAN Algorithm / Piotr Cal, Michał Woźniak
• Pattern Recognition and Image Processing in Robotics. Structured Light Techniques for 3D Surface Reconstruction in Robotic Tasks / M. Rodrigues [and others]
• Multi-modal People Detection from Aerial Video / Helen Flynn, Stephen Cameron.

• The Classification of the Terrain by a Hexapod Robot / Adam Schmidt, Krzysztof Walas
• Robust Registration of Kinect Range Data for Sensor Motion Estimation / Michał Nowicki, Piotr Skrzypczyński
• Utilization of Depth and Color Information in Mobile Robotics / Maciej Stefańczyk, Konrad Bojar, Włodzimierz Kasprzak
• Speech and Word Recognition. Texture-Based Text Detection in Digital Images with Wavelet Features and Support Vector Machines / Marcin Grzegorzek [and others]
• Automatic Disordered Syllables Repetition Recognition in Continuous Speech Using CWT and Correlation / Ireneusz Codello [and others]
• Evaluation of the Document Classification Approaches / Michal Hrala, Pavel Král
• The Prolongation-Type Speech Non-fluency Detection Based on the Linear Prediction Coefficients and the Neural Networks / Adam Kobus [and others].

This book is a study of computer recognition systems. It contains articles contributed by experts of pattern recognition, and reports on current research with respect to both methodology and applications. Topics include: biometrics; data stream classification and big data analytics; features, learning, and classifiers; image processing and computer vision; medical applications; pattern recognition and image processing in robotics; speech and word recognition.

• Awareness Platform.- Aalto Observatory on Digital Valuation Systems.- A Novel Lexicon-Based Approach in Determining Sentiment in Financial Data Using Learning Automata.- A Hybrid Recommendation System Based on Density-Based Clustering.- Computing Platform for Virtual Economic Activities Index.- Data Based Stock Portfolio Construction Using Computational Intelligence.- YourDataStories: Transparency and Corruption Fighting Through Data Interlinking and Visual Exploration.- The Maker Movement and the Disruption of the Producer-Consumer Relation.- Open Data: Creating Communities and Practices for a New Common.- The Case for Collaborative Policy Experimentation Using Advanced Geospatial Data Analytics and Visualisation.- An Engagement-Related Behaviour Change Approach for SavingFood in Greece.- Developing a Social Innovation Methodology in the Web 2.0 Era.- Code Hunting Games: a Mixed Reality Multiplayer Treasure Hunt Through a Conversational Interface.- Politician - an Imitation Game.- Towards Open Domain Chatbots - a GRU Architecture for Data Driven Conversations.- Creating Dialogues Using Argumentation and Social Practices.- An Overview of Open-Source Chatbots Social Skills.
• (source: Nielsen Book Data)

This book constitutes the refereed post-conference proceedings of 4 workshops, held at the 4th International Conference on Internet Science, Thessaloniki, Greece, in November 2017: the Second International Workshop on the Internet for Financial Collective Awareness and Intelligence, IFIN 2017, the International Workshop on Data Economy 2017, the International Workshop on Digital Technology to Support Social Innovation, DSI 2017, and the International Workshop on Chatbot Research and Design, CONVERSATIONS 2017. The 17 full papers presented together with one short paper were carefully reviewed and selected from 27 submissions. The contributions of the IFIN workshop focus on a multidisciplinary dialogue on how to use the internet to promote financial awareness and capability among citizens whereas the papers of the Data Economy workshop show how online data change economy and business. The aim of the DSI workshop was to collect the lessons learned from different platforms and settings, and to understand the requirements and challenges for building and using digital platforms to effectively engage broad participation in the social innovation process. The papers of the Conversations workshop explore the brave new world of human-computer communication through natural language, gathering latest developments in chatbots research and design.
(source: Nielsen Book Data)

• Information Systems Applications (incl.
• Internet).- Information Storage and Retrieval.- Software Engineering Artificial Intelligence (incl. Robotics).- Systems and Data Security.- Computer Appl. in Administrative Data
• Processing Ontologies.- Databases.- Semantics.- Cooperative Information Systems.-Cloud Computing.- Trust, Security, Privacy And Risk
• Management.
• (source: Nielsen Book Data)

This double volumes LNCS 11229-11230 constitutes the refereed proceedings of the Confederated International Conferences: Cooperative Information Systems, CoopIS 2018, Ontologies, Databases, and Applications of Semantics, ODBASE 2018, and Cloud and Trusted Computing, C&TC, held as part of OTM 2018 in October 2018 in Valletta, Malta. The 64 full papers presented together with 22 short papers were carefully reviewed and selected from 173 submissions. The OTM program every year covers data and Web semantics, distributed objects, Web services, databases, informationsystems, enterprise workflow and collaboration, ubiquity, interoperability, mobility, grid and high-performance computing.
(source: Nielsen Book Data)

This book constitutes the refereed post-conference proceedings of the 11th International Seminar on Speech Production, ISSP 2017, held in Tianjin, China, In October 2017. The 20 revised full papers included in this volume were carefully reviewed and selected from 68 submissions. They cover a wide range of speech science fields including phonology, phonetics, prosody, mechanics, acoustics, physiology, motor control, neuroscience, computer science and human interaction. The papers are organized in the following topical sections: emotional speech analysis and recognition; articulatory speech synthesis; speech acquisition; phonetics; speech planning and comprehension, and speech disorder.
(source: Nielsen Book Data)

• Next generation community engagement.- Online policy, politics and co-creation.- Understanding and empowering digital citizens.- Data-driven research and design.- Social media and online interaction.
• (source: Nielsen Book Data)

This book constitutes the proceedings of the 4th International Conference on Internet Science held in Thessaloniki, Greece, in November 2017. The 34 papers presented were carefully reviewed and selected for inclusion in this volume. They were organized in topical sections named: next generation community engagement; online policy, politics and co-creation; understanding and empowering digital citizens; data-driven research and design; social media and online interaction.
(source: Nielsen Book Data)

• OnTheMove 2017 Keynotes.- International Conference on Cooperative Information Systems (CoopIS) 2017.- Cloud and Trusted Computing (C&TC) 2017.- International Conference on Ontologies, DataBases, and Applications of Semantics (ODBASE) 2017.
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This double volumes LNCS 10573-10574 constitutes the refereed proceedings of the Confederated International Conferences: Cooperative Information Systems, CoopIS 2017, Ontologies, Databases, and Applications of Semantics, ODBASE 2017, and Cloud and Trusted Computing, C&TC, held as part of OTM 2017 in October 2017 in Rhodes, Greece. The 61 full papers presented together with 19 short papers were carefully reviewed and selected from 180 submissions. The OTM program every year covers data and Web semantics, distributed objects, Web services, databases, information systems, enterprise workflow and collaboration, ubiquity, interoperability, mobility, grid and high-performance computing.
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• Collective Awareness and Crowdsourcing Platforms
• Collaboration, Privacy and Conformity in Virtual/Social Environments
• Internet Interoperability, Freedom and Data Analysis
• Smart Cities and Sociotechnical Systems.

This book constitutes the proceedings of the Third International Conference on Internet Science held in Florence, Italy, in September 2016. The 25 papers presented were carefully reviewed and selected for inclusion in this volume. They were organized in topical sections named: collective awareness and crowdsourcing platforms¸ collaboration, privacy and conformity in virtual/social environments; internet interoperability, freedom and data analysis; smart cities and sociotechnical systems.

• OnTheMove 2017 Keynotes.- International Conference on Cooperative Information Systems (CoopIS) 2017.- Cloud and Trusted Computing (C&TC) 2017.- International Conference on Ontologies, DataBases, and Applications of Semantics (ODBASE) 2017.
• (source: Nielsen Book Data)

This double volumes LNCS 10573-10574 constitutes the refereed proceedings of the Confederated International Conferences: Cooperative Information Systems, CoopIS 2017, Ontologies, Databases, and Applications of Semantics, ODBASE 2017, and Cloud and Trusted Computing, C&TC, held as part of OTM 2017 in October 2017 in Rhodes, Greece. The 61 full papers presented together with 19 short papers were carefully reviewed and selected from 180 submissions. The OTM program every year covers data and Web semantics, distributed objects, Web services, databases, information systems, enterprise workflow and collaboration, ubiquity, interoperability, mobility, grid and high-performance computing.
(source: Nielsen Book Data)

• Pattern recognition and artificial intelligence techniques.- Image processing and analysis.- Robotics and remote sensing.- Natural language processing and recognition.- Applications of pattern recognition.
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This book constitutes the refereed proceedings of the 9th Mexican Conference on Pattern Recognition, MCPR 2017, held in Huatulco, Mexico, in June 2017. The 29 revised full papers presented were carefully reviewed and selected from 55 submissions. The papers are organized in topical sections on pattern recognition and artificial intelligence techniques, image processing and analysis, robotics and remote sensing, natural language processing and recognition, applications of pattern recognition.
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• Mobile web data analytics.- Big spatial data and urban computing.- Graph data management and analytics.- Mobility analytics from spatial and social data.
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This book constitutes the thoroughly refereed post-conference proceedings of the First APWeb-WAIM 2017 Workshops, held jointly with the First International Joint Conference APWeb-WAIM 2017, held in Beijing, China, in July 2017. The 25 full papers presented were carefully reviewed and selected from 40 submissions. The papers originating from six workshops present cutting-edge ideas, results, experiences, techniques, and tools from all aspects of web data management with the focus on mobile web data analytics; big spatial data and urban computing; graph data management and analytics; mobility analytics from spatial and social data.
(source: Nielsen Book Data)

This book constitutes the proceedings of the 11th International Conference on Internet and Distributed Computing Systems, IDCS 2018, held in Tokyo, Japan, in October 2018. The 21 full papers presented together with 5 poster and 2 short papers in this volume were carefully reviewed and selected from 40 submissions. This conference desired to look for inspiration in diverse areas (e.g., infrastructure and system design, software development, big data, control theory, artificial intelligence, IoT, self-adaptation, emerging models, paradigms, applications and technologies related to Internet-based distributed systems) to develop new ways to design and mange such complex and adaptive computation resources. .
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• Sections on motion and reconstruction.- Mathematical foundations and image processing.- Biomedical image analysis and applications.- Human pose analysis
• Recognition and scene understanding.
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This book constitutes the refereed proceedings of the 37th German Conference on Pattern Recognition, GCPR 2015, held in Aachen, Germany, in October 2015. The 45 revised full papers and one Young Researchers Forum presented were carefully reviewed and selected from 108 submissions. The papers are organized in topical sections on motion and reconstruction; mathematical foundations and image processing; biomedical image analysis and applications; human pose analysis; recognition and scene understanding.
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• to Pattern Recognition and Classification in Medical and Astrophysical Images.- Image Standardization and Enhancement.- Intensity and Region-Based Feature Recognition in Solar Images.- Advanced Feature Recognition and Classification Using Artificial Intelligence Paradigms.- Feature Recognition and Classification Using Spectral Methods.
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This book presents innovative techniques in recognition and classification of astrophysical and medical images. Coverage includes: image standardization and enhancement; region-based methods for pattern recognition in medical and astrophysical images; advanced information processing using statistical methods; and feature recognition and classification using spectral method.
(source: Nielsen Book Data)

• Conversational Agents in Healthcare: Using QCA to Explain Patients Resistance to Chatbots for Medication
• An Approach for Ex-Post-Facto Analysis of Knowledge Graph-Driven Chatbots
• the DBpedia Chatbot
• Privacy Concerns in Chatbot Interactions
• Creating Humanlike Chatbots: What Chatbot Developers Could Learn from Webcare Employees in Adopting a Conversational Human Voice
• The Conversational Agent "Emoty" Perceived by People with Neurodevelopmental Disorders: Is It a Human or a Machine
• Gender Bias in Chatbot Design
• Conversational Web Interaction: Proposal of a Dialog-Based Natural Language Interaction Paradigm for the Web
• Designing Chatbots for Guiding Online Peer Support Conversations for Adults with ADHD
• Towards Chatbots to Support Bibliotherapy Preparation and Delivery
• CivicBots
• Chatbots for Supporting Youth in Societal Participation
• Using Theory of Mind to Assess Users Sense of Agency in Social Chatbots
• Exploring Age Differences in Motivations for and Acceptance of Chatbot Communication in a Customer Service Context
• Improving Conversations: Lessons Learnt from Manual Analysis of Chatbot Dialogues
• Conversational Repair in Chatbots for Customer Service: The Effect of Expressing Uncertainty and Suggesting Alternatives
• Working Together with Conversational Agents: The Relationship of Perceived Cooperation with Service Performance Evaluation
• Chatbots for the Information Acquisition at Universities
• A Students View on the Application Area
• A Configurable Agent to Advance Peers Productive Dialogue in MOOCs
• Small Talk Conversations and the Long-Term Use of Chatbots in Educational Settings
• Experiences from a Field Study.

This book constitutes the refereed proceedings of the Third International Workshop on Chatbot Research and Design, CONVERSATIONS 2019, held in Amsterdam, The Netherlands, in November 2019. The 18 revised full papers presented in this volume were carefully reviewed and selected from 31 submissions. The papers are grouped in the following topical sections: user and communication studies user experience and design, chatbots for collaboration, chatbots for customer service, and chatbots in education.

• Structured Tensor Decompositions and Applications.- Matrix and Tensor Factorizations.- ICA Methods.- Nonlinear Mixtures.- Audio Data and Methods.- Signal Separation Evaluation Campaign.- Deep Learning and Data-driven Methods.- Advances in Phase Retrieval and Applications.- Sparsity-Related Methods.- Biomedical Data and Methods.
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This book constitutes the proceedings of the 14th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2018, held in Guildford, UK, in July 2018.The 52 full papers were carefully reviewed and selected from 62 initial submissions. As research topics the papers encompass a wide range of general mixtures of latent variables models but also theories and tools drawn from a great variety of disciplines such as structured tensor decompositions and applications; matrix and tensor factorizations; ICA methods; nonlinear mixtures; audio data and methods; signal separation evaluation campaign; deep learning and data-driven methods; advances in phase retrieval and applications; sparsity-related methods; and biomedical data and methods.
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• Biometrics.- Computer Vision Application.- Deep Learning.- Document Analysis.- Face Recognition and Analysis.- Feature Extraction and Selection.- Machine Learning.- Object Detection and Tracking.- Performance Evaluation and Database.- Remote Sensing.
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The four-volume set LNCS 11056, 110257, 11258, and 11073 constitutes the refereed proceedings of the First Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2018, held in Guangzhou, China, in November 2018. The 179 revised full papers presented were carefully reviewed and selected from 399 submissions. The papers have been organized in the following topical sections: Part I: Biometrics, Computer Vision Application. Part II: Deep Learning. Part III: Document Analysis, Face Recognition and Analysis, Feature Extraction and Selection, Machine Learning. Part IV: Object Detection and Tracking, Performance Evaluation and Database, Remote Sensing.
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• Behavior Analysis.- Human Motion Recognition.- Application Datasets.
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This book constitutes the revised selected papers of the Second International Workshop on Understanding Human Activities through 3D Sensors, UHA3DS 2016, that was held in conjunction with the 23rd International Conference on Pattern Recognition, ICPR 2016, held in Cancun, Mexico, in December 2016. The 9 revised full papers were carefully reviewed and selected from 12 submissions. The papers are organized in topical sections on Behavior Analysis, Human Motion Recognition, and Application Datasets.
(source: Nielsen Book Data)

• Biometrics.- Computer Vision Application.- Deep Learning.- Document Analysis.- Face Recognition and Analysis.- Feature Extraction and Selection.- Machine Learning.- Object Detection and Tracking.- Performance Evaluation and Database.- Remote Sensing.
• (source: Nielsen Book Data)

The four-volume set LNCS 11056, 110257, 11258, and 11073 constitutes the refereed proceedings of the First Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2018, held in Guangzhou, China, in November 2018. The 179 revised full papers presented were carefully reviewed and selected from 399 submissions. The papers have been organized in the following topical sections: Part I: Biometrics, Computer Vision Application. Part II: Deep Learning. Part III: Document Analysis, Face Recognition and Analysis, Feature Extraction and Selection, Machine Learning. Part IV: Object Detection and Tracking, Performance Evaluation and Database, Remote Sensing.
(source: Nielsen Book Data)

• Algorithms.- Biometrics and pattern recognition applications.- Data analysis and information retrieval.-Engineering of enterprise software products.- Industrial management and other applications.- Modelling and optimization.- Various aspects of computer security.
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This book constitutes the proceedings of the 16th IFIP TC8 International Conference on Computer Information Systems and Industrial Management, CISIM 2017, held in Bialystok, Poland, in June 2017. The 60 regular papers presented together with 5 keynotes were carefully reviewed and Selected from 85 submissions. They are organized in the following topical sections: algorithms; biometrics and pattern recognition applications; data analysis and information retrieval; engineering of enterprise software products; industrial management and other applications; modelling and optimization; various aspects of computer security.
(source: Nielsen Book Data)

• Dimensionality reduction.- Manifold learning and embedding methods.-Dissimilarity representations.- Graph-theoretic methods.- Model selection, classification and clustering.- Semi and fully supervised learning methods.- Shape analysis.- Spatio-temporal pattern recognition.- Structural matching.- Text and document analysis.
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This book constitutes the proceedings of the Joint IAPR International Workshop on Structural Syntactic, and Statistical Pattern Recognition, S+SSPR 2016, consisting of the International Workshop on Structural and Syntactic Pattern Recognition SSPR, and the International Workshop on Statistical Techniques in Pattern Recognition, SPR. The 51 full papers presented were carefully reviewed and selected from 68 submissions. They are organized in the following topical sections: dimensionality reduction, manifold learning and embedding methods; dissimilarity representations; graph-theoretic methods; model selection, classification and clustering; semi and fully supervised learning methods; shape analysis; spatio-temporal pattern recognition; structural matching; text and document analysis.
(source: Nielsen Book Data)

• Visualization and big data.- Machine learning and computer vision.- Computer graphics.- Virtual reality.
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This book constitute the refereed proceedings of the Fourth International Conference on Advances in Visual Informatics, IVIC 2015, held in Selangor, Malaysia, in November 2015. The five keynotes and 45 papers presented were carefully reviewed and selected from various submissions. The papers are organized in four tracks on visualization and big data; machine learning and computer vision; computer graphics; virtual reality.
(source: Nielsen Book Data)

• A Novel Data Representation based on a Second-Order Dissimilarity Measure.- Characterizing Multiple Instance Datasets.- Supervised learning of diffiusion distance to improve histogram matching.- Similarity Analysis from Limiting Quantum Walks.- Introducing Negative Evidence in Ensemble Clustering.- Dissimilarity representations for low-resolution face recognition.- Deep metric learning using Triplet network.- Cluster Merging Based on Dominant Sets.- An Adaptive Radial Basis Function Kernel for Support Vector Data Description.- Robust initialization for learning Latent Dirichlet Allocation.- Unsupervised Motion Segmentation Using Metric Embedding of Features.- Transitive Assignment Kernels for Structural Classification.- Large scale Indefinite Kernel Fisher Discriminant.- Similarity-based User Identification across Social Networks.- Dominant-Set Clustering Using Multiple Affinity Matrices.- Distance-Based Network Recovery under Feature Correlation.- Discovery of salient low-dimensional dynamical structure using Hopfield Networks.- On Geodesic Exponential Kernels.- A Matrix Factorization Approach to Graph Compression.- A Geometrical Approach to Find Corresponding Patches in 3D Medical Surfaces.- Similarities, SDEs, and Most Probable Paths.- Can the optimum similarity matrix be selected before clustering for graph-based approaches?.- Approximate spectral clustering with utilized similarity information fusing geodesic based hybrid distance measures.
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This book constitutes the proceedings of the Third International Workshop on Similarity Based Pattern Analysis and Recognition, SIMBAD 2015, which was held in Copenahgen, Denmark, in October 2015. The 15 full and 8 short papers presented were carefully reviewed and selected from 30 submissions.The workshop focus on problems, techniques, applications, and perspectives: from supervisedto unsupervised learning, from generative to discriminative models, and fromtheoretical issues to empirical validations.
(source: Nielsen Book Data)

• Biometrics.- Computer Vision Application.- Deep Learning.- Document Analysis.- Face Recognition and Analysis.- Feature Extraction and Selection.- Machine Learning.- Object Detection and Tracking.- Performance Evaluation and Database.- Remote Sensing.
• (source: Nielsen Book Data)

The four-volume set LNCS 11056, 110257, 11258, and 11073 constitutes the refereed proceedings of the First Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2018, held in Guangzhou, China, in November 2018. The 179 revised full papers presented were carefully reviewed and selected from 399 submissions. The papers have been organized in the following topical sections: Part I: Biometrics, Computer Vision Application. Part II: Deep Learning. Part III: Document Analysis, Face Recognition and Analysis, Feature Extraction and Selection, Machine Learning. Part IV: Object Detection and Tracking, Performance Evaluation and Database, Remote Sensing.
(source: Nielsen Book Data)

• Learning sequential data with the help of linear systems
• A spiking neural network for personalised modelling of Electrogastogrophy (EGG)
• Improving generalization abilities of maximal average margin classifiers
• Finding small sets of random Fourier features for shift-invariant kernel approximation
• Incremental construction of low-dimensional data representations
• Soft-constrained nonparametric density estimation with artificial neural networks
• Density based clustering via dominant sets
• Co-training with credal models
• Interpretable classifiers in precision medicine: feature selection and multi-class categorization
• On the evaluation of tensor-based representations for optimum-pathforest classification
• On the harmony search using quaternions
• Learning parameters in deep belief networks through firefly algorithm
• Towards effective classification of imbalanced data with convolutional neural networks
• On CPU performance optimization of restricted Boltzmann machine and convolutional RBM
• Comparing incremental learning strategies for convolutional neural networks
• Approximation of graph edit distance by means of a utility matrix
• Time series classification in reservoir- and model-space: a comparison
• Objectness scoring and detection proposals in forward-Looking sonar images with convolutional neural networks
• Background categorization for automatic animal detection in aerial videos using neural networks
• Predictive segmentation using multichannel neural networks in Arabic OCR system
• Quad-tree based image segmentation and feature extraction to recognize online handwritten Bangla characters
• A hybrid recurrent neural network/dynamic probabilistic graphical model predictor of the disulfide bonding state of cysteines from the primary structure of proteins
• Using radial basis function neural networks for continuous anddiscrete pain estimation from bio-physiological signals
• Active learning for speech event detection in HCI
• Emotion recognition in speech with deep learning architectures
• On gestures and postural behavior as a modality in ensemble methods
• Machine learning driven heart rate detection with camera photoplethysmography in time domain.

This book constitutes the refereed proceedings of the 7th IAPR TC3 International Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2016, held in Ulm, Germany, in September 2016. The 25 revised full papers presented together with 2 invited papers were carefully reviewed and selected from 32 submissions for inclusion in this volume. The workshop will act as a major forum for international researchers and practitioners working in all areas of neural network- and machine learning-based pattern recognition to present and discuss the latest research, results, and ideas in these areas.

• Robotics
• Computer vision
• Basic theory of pattern recognition
• Image and video processing
• Speech and language
• Emotion recognition.

The two-volume set CCIS 662 and CCIS 663 constitutes the refereed proceedings of the 7th Chinese Conference on Pattern Recognition, CCPR 2016, held in Chengdu, China, in November 2016. The 121 revised papers presented in two volumes were carefully reviewed and selected from 199 submissions. The papers are organized in topical sections on robotics; computer vision; basic theory of pattern recognition; image and video processing; speech and language; emotion recognition.

• Deep Learning Methods for Image Guidance in Radiation Therapy Intentional Image Similarity Search.- Sttructured (De)composable Representations Trained with Neural Networks.- Long Distance Relationships without Time Travel: Boosting the Performance of a Sparse Predictive Autoencoder in Sequence Modeling.- Improving Accuracy and Efficiency of Object Detection Algorithms using Multiscale Feature Aggregation Plugins.- Abstract Echo State Networks.- Minimal Complexity Support Vector Machines.- Named Entity Disambiguation at Scale.- Geometric Attention for Prediction of Differential Properties in 3D Point Clouds.- How (Not) to Measure Bias in Face Recognition Networks.-Feature Extraction: A Time Window Analysis based on the X-ITE Pain Database.- Pain Intensity Recognition - An Analysis of Short-Time Sequences in a Real-World Scenario.- A deep learning approach for efficient registration of dual view mammography.- Deep Transfer Learning for Texture Classification in Colorectal Cancer Histology.- Applications of Generative Adversarial Networks to Dermatologic Imaging.- Typing Plasmids with Distributed Sequence Representation.- KP-YOLO: a modification of YOLO algorithm for the keypoint-based detection of QR Codes.- Using Mask R-CNN for Image-Based Wear Classification of Solid Carbide Milling and Drilling Tools.- A Hybrid Deep Learning Approach For Forecasting Air Temperature.- Using CNNs to optimize numerical simulations in geotechnical engineering.- Going for 2D or 3D? Investigating various Machine Learning Approaches for Peach Variety Identification.- A Transfer Learning End-to-End Arabic Text-To-Speech (TTS) Deep Architecture.- ML-Based Trading Models: An investigation during COVID-19 pandemic crisis.- iNNvestigate-GUI - Explaining Neural Networks Through an Interactive Visualization Tool.
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This book constitutes the refereed proceedings of the 9th IAPR TC3 International Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2020, held in Winterthur, Switzerland, in September 2020. The conference was held virtually due to the COVID-19 pandemic. The 22 revised full papers presented were carefully reviewed and selected from 34 submissions. The papers present and discuss the latest research in all areas of neural network-and machine learning-based pattern recognition. They are organized in two sections: learning algorithms and architectures, and applications.
(source: Nielsen Book Data)