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• 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)

• pt. I. Technology. Robust layered control system for a mobile robot
• Robot that walks: emergent behaviors from a carefully evolved network
• Learning a distributed map representation based on navigation behaviors
• New approaches to robotics. pt. II. Philosophy. Intelligence without representation
• Planning is just a way of avoiding figuring out what to do next
• Elephants don't play chess
• Intelligence without reason.

Until the mid-1980s, AI researchers assumed that an intelligent system doing high-level reasoning was necessary for the coupling of perception and action. In this traditional model, cognition mediates between perception and plans of action. Realizing that this core AI, as it was known, was illusory, Rodney A. Brooks turned the field of AI on its head by introducing the behavior-based approach to robotics. The cornerstone of behavior-based robotics is the realization that the coupling of perception and action gives rise to all the power of intelligence and that cognition is only in the eye of an observer. Behavior-based robotics has been the basis of successful applications in entertainment, service industries, agriculture, mining, and the home. It has given rise to both autonomous mobile robots and more recent humanoid robots such as Brooks' Cog. This book represents Brooks' initial formulation of and contributions to the development of the behavior-based approach to robotics. It presents all of the key philosophical and technical ideas that put this "bottom-up" approach at the forefront of current research in not only AI but all of cognitive science.
(source: Nielsen Book Data)

• 1. The vision of sociable robots
• 2. Robot in society: a question of interface
• 3. Insights from developmental psychology
• 4. Designing sociable robots
• 5. The physical robot
• 6. The vision system
• 7. The auditory system
• 8. The motivation system
• 9. The behavior system
• 10. Facial animation and expression
• 11. Expressive vocalization system
• 12. Social constraints on animate vision
• 13. Grand challenges of building sociable robots.

Cynthia Breazeal here presents her vision of the sociable robot of the future, a synthetic creature and not merely a sophisticated tool. A sociable robot will be able to understand us, to communicate and interact with us, to learn from us and grow with us. It will be socially intelligent in a humanlike way. Eventually sociable robots will assist us in our daily lives, as collaborators and companions. Because the most successful sociable robots will share our social characteristics, the effort to make sociable robots is also a means for exploring human social intelligence and even what it means to be human. Breazeal defines the key components of social intelligence for these machines and offers a framework and set of design issues for their realization. Much of the book focuses on a nascent sociable robot she designed named Kismet. Breazeal offers a concrete implementation for Kismet, incorporating insights from the scientific study of animals and people, as well as from artistic disciplines such as classical animation. This blending of science, engineering, and art creates a lifelike quality that encourages people to treat Kismet as a social creature rather than just a machine. The book includes a CD-ROM that shows Kismet in action.
(source: Nielsen Book Data)

• Cover; Title page; Preface; Contents;
• 1. Introduction to Neural Networks for Instrumentation, Measurement, and Industrial Applications;
• 2. The Fundamentals of Measurement Techniques;
• 3. Neural Networks in Intelligent Sensors and Measurement Systems for Industrial Applications;
• 4. Neural Networks in System Identification;
• 5. Neural Techniques in Control;
• 6. Neural Networks for Signal Processing in Measurement Analysis and Industrial Applications: the Case of Chaotic Signal Processing.

This work aims to disseminate theoretical and practical knowledge about neural networks in measurement, instrumentation and the related industrial applications. It also creates a consciousness about the effectiveness of these techniques as well as the measurement problems in industrial environments.

• 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)

• 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.
• (source: Nielsen Book Data)

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)

• Introduction Rational Agents Strategic Games Coordination Partial Observability Mechanism Design Learning.
• (source: Nielsen Book Data)

Multiagent systems is an expanding field that blends classical fields like game theory and decentralized control with modern fields like computer science and machine learning. This monograph provides a concise introduction to the subject, covering the theoretical foundations as well as more recent developments in a coherent and readable manner. The text is centered on the concept of an agent as decision maker. Chapter 1 is a short introduction to the field of multiagent systems. Chapter 2 covers the basic theory of singleagent decision making under uncertainty. Chapter 3 is a brief introduction to game theory, explaining classical concepts like Nash equilibrium. Chapter 4 deals with the fundamental problem of coordinating a team of collaborative agents. Chapter 5 studies the problem of multiagent reasoning and decision making under partial observability. Chapter 6 focuses on the design of protocols that are stable against manipulations by self-interested agents. Chapter 7 provides a short introduction to the rapidly expanding field of multiagent reinforcement learning. The material can be used for teaching a half-semester course on multiagent systems covering, roughly, one chapter per lecture.
(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)

• 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)

• Humanoid Robotics.- Time-Varying Affective Response for Humanoid Robots.- The Co-simulation of Humanoid Robot Based on Solidworks, ADAMS and Simulink.- From RoboNova to HUBO: Platforms for Robot Dance.- BunnyBot: Humanoid Platform for Research and Teaching.- Teen Sized Humanoid Robot: Archie.- Interdisciplinary Construction and Implementation of a Human Sized Humanoid Robot by Master Students.- Human Robot Interaction.- Safety Aspects in a Human-Robot Interaction Scenario: A Human Worker Is Co-operating with an Industrial Robot.- Integration of a RFID System in a Social Robot.- A Practical Study on the Design of a User-Interface Robot Application.- Infrared Remote Control with a Social Robot.- BlogRobot: Mobile Terminal for Blog Browse Using Physical Representation.- An Exploratory Investigation into the Effects of Adaptation in Child-Robot Interaction.- Devious Chatbots - Interactive Malware with a Plot.- Towards Better Human Robot Interaction: Understand Human Computer Interaction in Social Gaming Using a Video-Enhanced Diary Method.- Promotion of Efficient Cooperation by Sharing Environment with an Agent Having a Body in Real World.- Interaction Design for a Pet-Like Remote Control.- Experiences with a Barista Robot, FusionBot.- Mutually Augmented Cognition.- How Humans Optimize Their Interaction with the Environment: The Impact of Action Context on Human Perception.- Development of a Virtual Presence Sharing System Using a Telework Chair.- PLEXIL-DL: Language and Runtime for Context-Aware Robot Behaviour.- Ambient Intelligence in a Smart Home for Energy Efficiency and Eldercare.- Education and Entertainment.- Intelligent Technologies for Edutainment Using Multiple Robots.- Remote Education Based on Robot Edutainment.- Not Just "Teaching Robotics" but "Teaching through Robotics".- A Proposal of Autonomous Robotic Systems Educative Environment.- Mechatronics Education: From Paper Design to Product Prototype Using LEGO NXT Parts.- Fostering Development of Students' Collective and Self-efficacy in Robotics Projects.- From an Idea to a Working Robot Prototype: Distributing Knowledge of Robotics through Science Museum Workshops.- Teaching Electronics through Constructing Sensors and Operating Robots.- Learning from Analogies between Robotic World and Natural Phenomena.- Integrating Robot Design Competitions into the Curriculum and K-12 Outreach Activities.- Teamwork and Robot Competitions in the Undergraduate Program at the Copenhagen University College of Engineering.- Cooperative Robotics.- Multiagents System with Dynamic Box Change for MiroSot.- Multi Block Localization of Multiple Robots.- Soty-Segment: Robust Color Patch Design to Lighting Condition Variation.- Task-Based Flocking Algorithm for Mobile Robot Cooperation.- Analysis of Spatially Limited Local Communication for Multi-Robot Foraging.- AMiRESot - A New Robot Soccer League with Autonomous Miniature Robots.- Robotic System Design.- BeBot: A Modular Mobile Miniature Robot Platform Supporting Hardware Reconfiguration and Multi-standard Communication.- System Design for Semi-automatic AndroSot.- Learning, Optimization, Communication.- Extended TA Algorithm for Adapting a Situation Ontology.- An Integer-Coded Chaotic Particle Swarm Optimization for Traveling Salesman Problem.- USAR Robot Communication Using ZigBee Technology.
• (source: Nielsen Book Data)

th This volume is an edition of the papers selected from the 12 FIRA RoboWorld C- gress, held in Incheon, Korea, August 16-18, 2009. The Federation of International Robosoccer Association (FIRA - www. fira. net) is a non-profit organization, which organizes robotic competitions and meetings around the globe annually. The RoboSoccer competitions started in 1996 and FIRA was - tablished on June 5, 1997. The Robot Soccer competitions are aimed at promoting the spirit of science and technology to the younger generation. The congress is a forum in which to share ideas and future directions of technologies, and to enlarge the human networks in robotics area. The objectives of the FIRA Cup and Congress are to explore the technical dev- opment and achievement in the field of robotics, and provide participants with a robot festival including technical presentations, robot soccer competitions and exhibits - der the theme "Where Theory and Practice Meet. " th Under the umbrella of the 12 FIRA RoboWorld Incheon Congress 2009, six int- national conferences were held for greater impact and scientific exchange: th * 6 International Conference on Computational Intelligence, Robotics and Autonomous Systems (CIRAS) th * 5 International Symposium on Autonomous Minirobots for Research and Edutainment (AMiRE) * International Conference on Social Robotics (ICSR) * International Conference on Advanced Humanoid Robotics Research (ICAHRR) * International Conference on Entertainment Robotics (ICER) * International Robotics Education Forum (IREF) This volume consists of selected quality papers from the six conferences.
(source: Nielsen Book Data)

• 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)

• Cognitive Systems Introduction.- Component Science.- Architecture and Representations.- The Sensorimotor Approach in CoSy: The Example of Dimensionality Reduction.- Categorical Perception.- Semantic Modelling of Space.- Planning and Failure Detection.- Multi-modal Learning.- Situated Dialogue Processing for Human-Robot Interaction.- Integration and Systems.- The PlayMate System.- The Explorer System.- Lessons Learnt from Scenario-Based Integration.- Summary and Outlook.- Cross-Disciplinary Reflections: Philosophical Robotics.- Lessons and Outlook.
• (source: Nielsen Book Data)

Design of cognitive systems for assistance to people poses a major challenge to the fields of robotics and artificial intelligence. The Cognitive Systems for Cognitive Assistance (CoSy) project was organized to address the issues of i) theoretical progress on design of cognitive systems ii) methods for implementation of systems and iii) empirical studies to further understand the use and interaction with such systems. To study, design and deploy cognitive systems there is a need to considers aspects of systems design, embodiment, perception, planning and error recovery, spatial insertion, knowledge acquisition and machine learning, dialog design and human robot interaction and systems integration. The CoSy project addressed all of these aspects over a period of four years and across two different domains of application - exploration of space and task / knowledge acquisition for manipulation. The present volume documents the results of the CoSy project. The CoSy project was funded by the European Commission as part of the Cognitive Systems Program within the 6th Framework Program.
(source: Nielsen Book Data)

• 1. Introduction
• 2. Clusters and robots
• 3. Cluster programming
• 4. Robot motion
• 5. Sensors
• 6. Mapping and localization
• 7. Vision and tracking
• 8. Learning landmarks
• 9. Robot architectures
• Appendix I: Summary of OpenMPI man page for mpirun
• Appendix II: MPI datatypes
• Appendix III: MPI reduction operations
• Appendix IV: MPI application programmer interface.

In this book, we look at how cluster technology can be leveraged to build better robots. Algorithms and approaches in key areas of robotics and computer vision, such as map building, target tracking, action selection and landmark learning, are reviewed and cluster implementations for these are presented.The objective of the book is to give professionals working in the beowulf cluster or robotics and computer vision fields a concrete view of the strong synergy between the areas as well as to spur further fruitful exploitation of this connection. The book is written at a level appropriate for an advanced undergraduate or graduate student. The key concepts in robotics, computer vision and cluster computing are introduced before being used to make the text useful to a wide audience in these fields.
(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)

• 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)

• 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.

• 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.

• 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.