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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)