%{search_type} search results

• 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)

• 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)

• 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)

• 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.

• 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)

• 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.

• Computer graphics.-Applications
• Visual Surveillance
• Virtual Reality.

The two volume set LNCS 10072 and LNCS 10073 constitutes the refereed proceedings of the 12th International Symposium on Visual Computing, ISVC 2016, held in Las Vegas, NV, USA in December 2016. The 102 revised full papers and 34 poster papers presented in this book were carefully reviewed and selected from 220 submissions. The papers are organized in topical sections: Part I (LNCS 10072) comprises computational bioimaging; computer graphics; motion and tracking; segmentation; pattern recognition; visualization; 3D mapping; modeling and surface reconstruction; advancing autonomy for aerial robotics; medical imaging; virtual reality; computer vision as a service; visual perception and robotic systems; and biometrics. Part II (LNCS 9475): applications; visual surveillance; computer graphics; and virtual reality.

• 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)

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)

• 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.

• Workshop on Emotions, Modality, Sentiment Analysis and theSemantic Web.- 5th Workshop on Knowledge Discovery and Data Mining MeetsLinked Open Data (Know@LOD).- LDQ: 3rd Workshop on Linked Data Quality.- Fourth Workshop on Linked Media (LiME-2016).- Managing the Evolution and Preservation of the Data Web.- PROFILES
• '16: 3rd International Workshop on Dataset PROFIling and fEderated Search for Linked Data.- Workshop on Extraction and Processing of Rich Semantics from Medical Texts.- SALAD - Services and Applications over Linked APIs and Data.- Semantic Web Technologies in Mobile and Pervasive Environments (SEMPER).- International Workshop on Summarizing and Presenting Entities and Ontologies.- 2nd Int. Workshop on Semantic Web for Scientific Heritage (SW4SH).- 1st Workshop on Humanities in the SEmantic web (WHiSE 2016).
• (source: Nielsen Book Data)

This book constitutes the thoroughly refereed post-conference proceedings of the Satellite Events of the 13th European Conference on the Semantic Web, ESWC 2016, held in Heraklion, Greece, in May/June 2016. The volume contains 16 full papers and 38 poster and demonstration papers, carefully selected from 12 workshops focusing on specific research issues related to the Semantic Web.
(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.

• 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.

• Network Element Stability Aware Method for Verifying Configuration Changes in Mobile Communication Networks
• A Framework for Publish/Subscribe Protocol Transitions in Mobile Crowds
• Cloud Flat Rates Enabled via Fair Multi-resource Consumption
• Decentralized Solutions for Monitoring Large-Scale Software-Defined Networks
• S3N
• Smart Solution for Substation Networks, an Architecture for the Management of Communication Networks in Power Substations
• Towards a QoS-oriented Migration Management Approach for Virtualized Network
• Functional Decomposition in 5G Networks
• An NFC Relay Attack with Off-the-Shelf Hardware and Software
• Analysis and Evaluation of OpenFlow Message Usage for Security Applications
• On the Readiness of NDN for a Secure Deployment: The Case of Pending Interest Table
• In Whom Do We Trust
• Sharing Security Events
• Network Defence Using Attacker-Defender Interaction Modelling
• Evaluating Reputation of Internet Entities
• Detecting Advanced Network Threats Using Similarity Search
• How to Achieve Early Botnet Detection at the Provider Level?
• Anycast and Its Potential for DDoS Mitigation
• Context-Aware Location Management of Groups of Devices in 5G Networks
• Scalability and Information Exchange among Autonomous Resource Management Agents
• Analysis of Vertical Scans Discovered by Naive Detection.

This book constitutes the refereed proceedings of the 10th IFIP WG 6.6 International Conference on Autonomous Infrastructure, Management, and Security, AIMS 2016, held in Munich, Germany, in June 2016. The 7 full papers presented together with 3 short papers were carefully reviewed and selected from 22 submissions. The volume also includes 9 papers presented at the AIMS PhD workshop. They were reviewed in a separate process and selected from 21 submissions. The full papers are organized in topical sections on autonomic and smart management and security attacks and defenses. The workshop papers are organized in topical sections on management of future networks and security management. The short papers deal with methods for management and security.

• Computer vision and image analysis
• Pattern recognition and artificial intelligent techniques
• Signal processing and analysis
• Applications of pattern recognition.

This book constitutes the refereed proceedings of the 8th Mexican Conference on Pattern Recognition, MCPR 2016, held in Guanajuato, Mexico, in June 2016. The 34 revised full papers presented were carefully reviewed and selected from 60 submissions. The papers are organized in topical sections on computer vision and image analysis; pattern recognition and artificial intelligent techniques; signal processing and analysis; and applications of pattern recognition.

• Data analytics and optimization for assessing a ride sharing system.- Constraint-Based Querying for Bayesian Network Exploration.- Efficient Model Selection for Regularized Classification by Exploiting Unlabeled Data.- Segregation Discovery in a Social Network of Companies.- A first+-order-logic based model for grounded language learning.- A parallel distributed processing algorithm for image feature extraction.- Modeling concept drift: A probabilistic graphical model based approach.- Diversity-driven Widening of Hierarchical Agglomerative Clustering.- Batch Steepest-Descent-Mildest-Ascent for Interactive Maximum Margin Clustering.- Time Series Classification with Representation Ensembles.- Simultaneous Clustering and Model Selection for Multinomial Distribution: A Comparative Study.- On Binary Reduction of Large-scale Multiclass Classification Problems.- Probabilistic Active Learning in Data Streams.- Implicitly Constrained Semi-Supervised Least Squares Classification.- Diagonal Co-clustering Algorithm for Document-Word Partitioning.- I-Louvain: an attributed graph clustering method.- Class-based outlier detection: staying zombies or awaiting for resurrection?.- Using Metalearning for Prediction of Taxi Trip Duration Using Different Granularity Levels.- Using entropy as a measure of acceptance for multi-label classification.- Investigation of Node Deletion Techniques for Clustering Applications of Growing Self Organizing Maps.- Exploratory topic modeling with distributional semantics.- Assigning Geo-Relevance of Sentiments Mined from Location-Based Social Media Posts.- Continuous and Discrete Deep Classifiers for Data Integration.- A Bayesian Approach for Identifying Multivariate Differences Between Groups.- Automatically Discovering Offensive Patterns in Soccer Match Data.- Fast Algorithm Selection using Learning Curves.- Optimally Weighted Cluster Kriging for Big Data Regression.- Slower can be faster: The iRetis incremental model tree learner.- VoQs: A Web Application for Visualization of Questionnaire Surveys.
• (source: Nielsen Book Data)

This book constitutes the refereed conference proceedings of the 14th International Conference on Intelligent Data Analysis, which was held in October 2015 in Saint Etienne. France. The 29 revised full papers were carefully reviewed and selected from 65 submissions. The traditional focus of the IDA symposium series is on end-to-end intelligent support for data analysis. The symposium aims to provide a forum for inspiring research contributions that might be considered preliminary in other leading conferences and journals, but that have a potentially dramatic impact. To facilitate this, IDA 2015 will feature two tracks: a regular "Proceedings" track, as well as a "Horizon" track for early-stage research of potentially ground-breaking nature.
(source: Nielsen Book Data)

• Sections on motion and reconstruction.- Mathematical foundations and image processing.- Biomedical image analysis and applications.- Human pose analysis
• Recognition and scene understanding.
• (source: Nielsen Book Data)

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.
(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.
• (source: Nielsen Book Data)

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)

• Visualization and big data.- Machine learning and computer vision.- Computer graphics.- Virtual reality.
• (source: Nielsen Book Data)

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)