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• Getting started
• Perceptron learning -- basics
• Choice of learning rules
• Augmented statistical mechanics formulation
• Noisy teachers
• Storage problem
• Discontinuous learning
• Unsupervised learning
• On-line learning
• Making contact with statistics
• Bird's eye view: multifractals
• Multilayer networks
• On-line learning in multilayer networks
• What else?

Learning is one of the things that humans do naturally, and it has always been a challenge for us to understand the process. Nowadays this challenge has another dimension as we try to build machines that are able to learn and to undertake tasks such as datamining, image processing and pattern recognition. We can formulate a simple framework, artificial neural networks, in which learning from examples may be described and understood. The contribution to this subject made over the last decade by researchers applying the techniques of statistical mechanics is the subject of this book. The authors provide a coherent account of various important concepts and techniques that are currently only found scattered in papers, supplement this with background material in mathematics and physics and include many examples and exercises to make a book that can be used with courses, or for self-teaching, or as a handy reference.
(source: Nielsen Book Data)

• 1. Introduction
• 2. The strategic-negotiation model
• 3. Negotiations about data allocation
• 4. Negotiations about resource allocation
• 5. Negotiations about resource allocation with multiple attributes
• 6. Negotiations about task distribution
• 7. Negotiations about how to reduce pollution
• 8. Negotiation during a hostage crisis
• 9. Economic and game-theoretic models for cooperation
• 10. Conclusions and future directions.

A model for strategic negotiation for intelligent agents. As computers advance from isolated workstations to linked elements in complex communities of systems and people, cooperation and coordination via intelligent agents become increasingly important. Examples of such communities include the Internet, electronic commerce, health institutions, electricity networks, and digital libraries. Sarit Kraus is concerned here with the cooperation and coordination of intelligent agents that are self-interested and usually owned by different individuals or organizations. Conflicts frequently arise, and negotiation is one of the main mechanisms for reaching agreement. Kraus presents a strategic-negotiation model that enables autonomous agents to reach mutually beneficial agreements efficiently in complex environments. The model, which integrates game theory, economic techniques, and heuristic methods of artificial intelligence, can be automated in computer systems or applied to human situations. The book provides both theoretical and experimental results.
(source: Nielsen Book Data)

The book includes all the background material required to understand the principles underlying intelligence, as well as enough detailed information on intelligent robotics and simulated agents so readers can begin experiments and projects on their own. By the mid-1980s researchers from artificial intelligence, computer science, brain and cognitive science, and psychology realized that the idea of computers as intelligent machines was inappropriate. The brain does not run "programs"; it does something entirely different. But what? Evolutionary theory says that the brain has evolved not to do mathematical proofs but to control our behavior, to ensure our survival. Researchers now agree that intelligence always manifests itself in behavior-thus it is behavior that we must understand. An exciting new field has grown around the study of behavior-based intelligence, also known as embodied cognitive science, "new AI, " and "behavior-based AI." This book provides a systematic introduction to this new way of thinking. After discussing concepts and approaches such as subsumption architecture, Braitenberg vehicles, evolutionary robotics, artificial life, self-organization, and learning, the authors derive a set of principles and a coherent framework for the study of naturally and artificially intelligent systems, or autonomous agents. This framework is based on a synthetic methodology whose goal is understanding by designing and building. The book includes all the background material required to understand the principles underlying intelligence, as well as enough detailed information on intelligent robotics and simulated agents so readers can begin experiments and projects on their own. The reader is guided through a series of case studies that illustrate the design principles of embodied cognitive science.
(source: Nielsen Book Data)

• Consideration of emotion model and primitive language of robots, T. Ogata and S. Sugano
• an architecture for animal-like behaviour selection, T. Kitamura
• a computation literary theory -the ultimate products of the brain/mind machine, A. Tokosumi
• cooperation between neural networks within the brain, M. Dufosse et al
• brain-like functions in evolving connectionist systems for on-line, knowledge-based learning, N. Kasabov
• interrelationships, communication, semiotics and artificial consciousness, H.-N.L. Teodorescu
• time emerges from incomplete clock, based on internal measurement, Y.-P. Gunji et al
• the logical jump in shell changing in hermit crab and tool experiment in ants, M. Kitabayashi et al
• the neurobiology of semantics - how can machines be designed to have meanings, W.J. Freeman
• the emergence of contentful experience, M.H. Bickhard
• intentionality and foundations of logic - a new approach to neurocomputation, G. Basti.
• (source: Nielsen Book Data)

This volume is a guide to two types of transcendence of academic borders which seem necessary for understanding and modelling brain function. The first type is technical transcendence needed to make intelligent machines such as a humanoid robot, an animal-like behavior architecture, an interpreter of fiction, and an evolving learning machine. This technical erosion is conducted into areas such as biology, ethology, neuroscience and psychology, as well as robotics and soft computing. The second type of transcendence of cross-disciplinary boundaries cuts across scientific areas such as biology and cognitive science/philosophy, into comprehensive, less technical and more abstract aspects of brain function. These aspects enable us to know in what direction and how far an intelligent machine will go.
(source: Nielsen Book Data)

• Consideration of emotion model and primitive language of robots, T. Ogata and S. Sugano
• an architecture for animal-like behaviour selection, T. Kitamura
• a computation literary theory -the ultimate products of the brain/mind machine, A. Tokosumi
• cooperation between neural networks within the brain, M. Dufosse et al
• brain-like functions in evolving connectionist systems for on-line, knowledge-based learning, N. Kasabov
• interrelationships, communication, semiotics and artificial consciousness, H.-N.L. Teodorescu
• time emerges from incomplete clock, based on internal measurement, Y.-P. Gunji et al
• the logical jump in shell changing in hermit crab and tool experiment in ants, M. Kitabayashi et al
• the neurobiology of semantics - how can machines be designed to have meanings, W.J. Freeman
• the emergence of contentful experience, M.H. Bickhard
• intentionality and foundations of logic - a new approach to neurocomputation, G. Basti.
• (source: Nielsen Book Data)

This volume is a guide to two types of transcendence of academic borders which seem necessary for understanding and modelling brain function. The first type is technical transcendence needed to make intelligent machines such as a humanoid robot, an animal-like behavior architecture, an interpreter of fiction, and an evolving learning machine. This technical erosion is conducted into areas such as biology, ethology, neuroscience and psychology, as well as robotics and soft computing. The second type of transcendence of cross-disciplinary boundaries cuts across scientific areas such as biology and cognitive science/philosophy, into comprehensive, less technical and more abstract aspects of brain function. These aspects enable us to know in what direction and how far an intelligent machine will go.
(source: Nielsen Book Data)

• Cover; Title page; Preface; Acknowledgments; Contents; I. Data Collection; Toward Active Mining from On-line Scientific Text Abstracts Using Pre-existing Sources; Data Mining on the WAVEs
• Word-of-mouth-Assisting Virtual Environments; Immune Network-based Clustering for WWW Information Gathering/Visualization; Interactive Web Page Retrieval with Relational Learning-based Filtering Rules; Monitoring Partial Update of Web Pages by Interactive Relational Learning; Context-based Classification of Technical Terms Using Support Vector Machines.

Focusing on data mining, this work is a joint effort from researchers in Japan, and includes a report on the forefront of data collection, user-centred mining and user interaction/reaction. It offers an overview of modern solutions with real-world applications, sharing hard-learned experiences.

• Cover; Title page; Contents; Retriever Prototype of a Case Based Reasoning: A Study Case; Dynamic Compaction Process of Metal Powder Media within Dies; Automated Theorem Proving for Many-sorted Free Description Theory Based on Logic Translation; Annotated Logic and Negation as Failure; Multi-agent System for Distribution System Operation; ArTbitrariness: Putting Computer Creativity to Work in Aesthetic Domains; An Overview of Fuzzy Numbers and Fuzzy Arithmetic; The Brain and Arithmetic Calculation; Evolving Arithmetical Knowledge in a Distributed Intelligent Processing System.

Logic (both classical and non-classical) is being increasingly related with other fields in almost every scientific discipline and human activity. This work covers its role in artificial intelligence, robotics, informatics, technology, and correlated themes.

The annual conference on Neural Information Processing Systems (NIPS) is the flagship conference on neural computation. The conference is interdisciplinary, with contributions in algorithms, learning theory, cognitive science, neuroscience, vision, speech and signal processing, reinforcement learning and control, implementations, and diverse applications. Only about 30 percent of the papers submitted are accepted for presentation at NIPS, so the quality is exceptionally high. These proceedings contain all of the papers that were presented at the 2001 conference.
(source: Nielsen Book Data)

• Part 1 - Setting the Stage
• Chapter 1 - Intelligent Machines: Imitating Life
• Chapter 2 - Deep Blue: A Triumph of AI?
• Chapter 3 - Building An Artificial Brain
• Chapter 4 - Evolutionary Computation: Putting Nature to Work
• Chapter 5 - Blue Hawaii: Natural Selection
• Chapter 6 - Checkers
• Chapter 7 - Chinook: The Man-machine Checkers Champion
• Chapter 8 - Samuel's Learning Machine
• Chapter 9 - The Samuel-Newell Challenge
• Part 2 - The Making of Blondie
• Chapter 10 - Evolving in the Checkers Environment
• Chapter 11 - In The Zone
• Chapter 12 - A Repeat Performance
• Chapter 13 - A New Dimension
• Chapter 14 - Letting the Genie Out of the Bottle
• Chapter 15 - Blondie24 Epilogue: The Future of Artificial Intelligence Appendix: Your Honor, I Object! Notes Index About the Author.
• (source: Nielsen Book Data)

Blondie24 tells the story of a computer that taught itself to play checkers far better than its creators ever could by using a program that emulated the basic principles of Darwinian evolution--random variation and natural selection-- to discover on its own how to excel at the game. Unlike Deep Blue, the celebrated chess machine that beat Garry Kasparov, the former world champion chess player, this evolutionary program didn't have access to strategies employed by human grand masters, or to databases of moves for the endgame moves, or to other human expertise about the game of chekers. With only the most rudimentary information programmed into its "brain, " Blondie24 (the program's Internet username) created its own means of evaluating the complex, changing patterns of pieces that make up a checkers game by evolving artificial neural networks---mathematical models that loosely describe how a brain works. It's fitting that Blondie24 should appear in 2001, the year when we remember Arthur C. Clarke's prediction that one day we would succeed in creating a thinking machine. In this compelling narrative, David Fogel, author and co-creator of Blondie24, describes in convincing detail how evolutionary computation may help to bring us closer to Clarke's vision of HAL. Along the way, he gives readers an inside look into the fascinating history of AI and poses provocative questions about its future.
(source: Nielsen Book Data)

• FOREWORD; CONTENTS; Keynote Address; The Origin of Complexity (presented, but paper not submitted); Plenary Session; The Role of Field Coupling in Nano-Scale Cellular Nonlinear Networks; Session Theory I; On the Relationship Between CNNs and PDEs; New Spatial-Temporal Patterns and the First Programmable On-Chip Bifurcation Test-Bed; On Stability of Full Range and Polynomial Type CNNs; A Study on Limit Cycles in Nearly Symmetric Cellular Neural Networks; An Improved Global Stability Result for Cellular Neural Networks with Time Delay; Session Applications I.

• SC-CNNs for Chaotic Signals GenerationMoving Object Tracking on Panoramic Images; MPEG-4 Based Modifications for an CNN Segmentation Chip; Watermarking for the Authentication of Video on CNN-UM; An Analogic CNN-Algorithm of Pixel Level Snakes for Tracking and Surveillance Tasks; Session Theory II; On the Dynamics of a Class of Cellular Neural Networks; mL-CNN: A CNN Model for Reaction-Diffusion Processes in m-Component Systems; Emergence of Global Patterns in Connected Neural Networks; Influence of System Non-Uniformity on Dynamic Phenomena in Arrays of Coupled Nonlinear Networks.

• Boolean Design of Binary Initialized and Coupled CNN Image Processing OperatorsSession Physical Implementations I; ACE16K: A 128x128 Focal Plane Analog Processor with Digital I/O.; On the RTD Implementation of Simplicial Cellular Nonlinear Networks; Programmable Optical CNN Implementation Based on the Template Pixels' Angular Coding; Application Issues Of A Programmable Optical CNN Implementation; Configurable Multi-Layer CNN-UM Emulator on FPGA; Session Applications II (Poster); Object-Oriented Image Analysis via Analogic CNN Algorithms
• Part I: Motion Estimation.

• Object-Oriented Image Analysis via Analogic CNN Algorithms
• Part II: Image Synthesis and Consistency ObservationA CNN Path Planning for a Mobile Robot in an Environment with Obstacles; A CNN Based System to Blind Sources Separation of MEG Signals; Delay-Driven Contrast Enhancement Using a Cellular Neural Network with State-Dependant Delay; Plenary Session; Time as Coding Space for Information Processing in the Cerebral Cortex; Characterizing the Spatio-Temporal Dynamics of the Epileptogenic Process with Nonlinear EEG Analyses; Session Bionics and Biologically Relevant Models I.

• Analyzing Multidimensional Neural Activity via CNN-UMBasic Mammalian Retinal Effects on the Prototype Complex Cell CNN Universal Machine; Prediction of Epileptic Seizures by CNN with Linear Weight Functions; Biometric Authentication Based on Perceptual Resonance between CNN Emergent Patterns and Humans; CNN Based Central Pattern Generators with Sensory Feedback; Session Integrated Sensing and Processing; Mobile Sensor-Actuator Networks: Opportunities and Challenges; Visual Feedback by Using a CNN Chip Prototype System; Multi-Target Tracking with Stored Program Adaptive CNN Universal Machines.

This volume covers the fundamental theory of Cellular Neural Networks as well as their applications in various fields such as science and technology. It contains all 83 papers of the 7th International Workshop on Cellular Neural Networks and their Applications. The workshop follows a biennial series of six workshops consecutively hosted in Budapest (1990), Munich, Rome, Seville, London and Catania (2000).

• Invited lectures. Trends in computational intelligence in nuclear engineering / R.E. Uhrig
• Possibility theory in decision support systems / H. Prade
• Computing with words-semantics / P.P. Wang
• pt. 1. Recent developments. Computing with words: the concept of conformity / M. Gemeinder
• Semantic additive and non additive measure as modal logic model of fuzzy sets and fuzzy measure / G. Resconi and I.B. Türksen
• A new lattice-valued propositional logic (I): semantics / K. Qin and Y. Xu
• Fuzzy numerical methods / I. Perfilieva
• Solution of Dirichlet boundary value problem for the Poisson equation based on a fuzzy system / B.S. Moon [and others]
• A strong relevant logic approach to the calculus of fuzzy conditionals / J. Cheng and Y. Goto
• The [symbol]-resolution field of indecomposable extremely simple form of lattice-valued propositional logic LP(X) / W. Wang [and others]
• Fuzzy relation equations via basic predicate fuzzy logic / V. Novák, I. Perfilieva and S. Gottwald
• Underlying criteria in valued preference relations / J. Montero [and others]
• Model predictive control using fuzzy satisfactory optimization / S. Li and W. Qu
• Mirror strategy in game playing: combining fuzzy sets and genetic algorithm to improve strategies / A. Cincotti, V. Cutello and G. Sorace
• The extraction of linguistic knowledge using fuzzy logic and generalized quantifiers / A. Dvorák and V. Novák
• A speed improvement scheme in knowledge discovery of association rule algorithm for a market basket / W. Premchaiswadi [and others]
• Dynamic clustering based on maximum frequent itemsets for analysing purchase pattern / Y. Zhao and P. Shi
• Using soft computing techniques to integrate multiple kinds of attributes in data mining / S. Coppock and L. Mazlack
• Discovering association rules with degrees of support and implication (ARsi) / G. Chen [and others]
• The role of Occam's razor in activity based modeling / E. Moons [and others]
• Reliability demonstration of interior-outer-set mode relevant in risk assessment / C. Huang
• Flexibility quantification in computer integrated manufacturing systems based on fuzzy cash flow analysis / A. Beskese, C. Kahraman and D. Ruan
• About the fusion of technical-specification knowledge components / E. Grégoire
• Information diffusion method in risk analysis / H. Strang [and others]
• The theory of optimal information diffusion estimation and its application / X. Wang and Y. You
• Risk factor analysis and evaluation of natural disasters: application of the RIFAE framework to the 2000 TokaiFlood Disaster in Japan / G. Zhai [and others]
• Towards a soft representation of diversity in reliability theory / M. Oussalah and L. Strigini
• Analogy between multistate system and nonadditive reliability models / M. Oussalah and M. Newby.

• pt. 2. Computational intelligent systems. An architecture model of intelligent agents system to approach problems of distributed knowledge / F. Llorens, R. Rizo and M. Pujol
• Design of a time-delay feedback controller to chaotify continuous-time Takagi-Sugeno fuzzy systems / Z. Li and B. Zhang
• On-line inference for fuzzy controllers in continuous domains / S. Lu
• Generalized predictive control with fuzzy constraints simulation / S. Li, G. Du and W. Qu
• Process identification through modular neural networks and rule extraction / B.J. van der Zwaag, K. Slump and L. Spaanenburg
• Automated image enhancement algorithm based on optimization of generalized fuzzy entropies (GFEs) / I.K. Vlachos and G.D. Sergiadis
• Integration of human knowledge and measured data for optimization of fabric hand / X. Zeng [and others]
• Fuzzy hidden Markov models for online training evaluation in virtual reality simulators / R.M. de Moraes and L. dos Santos Machado
• A new approach for plant monitoring using type-2 fuzzy logic and fractal theory / O. Castillo and P. Melin
• Integration of fuzzy techniques and perception systems for ITS / R. Garcia [and others]
• Digital neural networks in the CAM-brain machine / H. Eeckhaut and J. Van Campenhout
• Hysteresis modelling using feed-forward neural networks and its application to soft magnetic materials / D. Makaveev, L. Dupré and J. Melkebeek
• A new method for adaptive control of non-linear plants using type-2 fuzzy logic and neural networks / P. Melin and O. Castillo
• Analysis and modeling of field device tool interfaces / W. Zhang and C. Diedrich
• Comparing a genetic fuzzy and a neurofuzzy classifier for credit scoring / F. Hoffmann [and others]
• A modular neural network classifier for the recognition of occluded characters in automatic license plate reading / J.A.G. Nijhuis, A. Broersma and L. Spaanenburg
• Noise cleaning process using the fuzzy membership function / A. Thammano and P. Ruxpakawong
• A hybrid scheme, ART1 and feature matching, for Thai character recognition systems / N. Premchaiswadi [and others]
• Environmental systems modelling and diagnosis using a multiple model approach / K. Gasso, G. Mourot and J. Ragot
• Risk assessment of avalanches
• a fuzzy GIS application / M. Hemetsberger [and others]
• Land evaluation for surface irrigation using fuzzy representation and aggregation schemes / A. Tocatlidou [and others]
• Annoyance prediction with fuzzy rule bases / A. Verkeyn and D. Botteldooren.

• pt. 3. Applied research and nuclear applications. Real-time intelligent vision sensor for robot navigation using symmetry features / D. Popescu, K. Huebner and J. Zhang
• Self-organizing fuzzy system for vision-based position estimation / N. Popescu and J. Zhang
• Fuzzy adaptive robust tracking control for a class of uncertain nonlinear systems and its application / Y. Yang and C. Zhou
• Motion control of an hexapod walking machine using fuzzy state automata / D. Morano and L.M. Reyneri
• Equivalent linearization of 2-way fuzzy adaptive system under nonparametric uncertainty and inconsistency / E. Gurkan [and others]
• Adaptive fuzzy neural control of nonlinear systems / Y. Gao, M.J. Er and C. Deng
• Toward perception-based robotics: a fuzzy-arithmetic-based Lyapunov synthesis approach / C. Zhou, P.K. Yue and H.L. Sng
• Algorithmic lateral inhibition as a generic method for visual information processing with potential applications in robotics / A.E. Delgado and J. Mira
• A reinforcement learning method for dynamic obstacle avoidance in robotic mechanisms / D. Maravall and J. de Lope
• Intelligent navigation in partially unknown indoor environments / A. Poncela [and others]
• Evolutionary active force control of a 5-link biped robot / L.C. Kwek [and others]
• Synthesis and evaluation analysis of the physical model indicator information by computing with words / J. Liu, D. Ruan and R. Carchon
• Fuzzy-logic supported evaluation of the disposal costs and tariffs of high-level radioactive waste / P.L. Kunsch, A. Fiordaliso and P. Fortemps
• Designing reduced scale thermal-hydraulic experiments using genetic algorithms / C.M.F. Lapa, P.A.B. de Sampaio and C.M.N.A. Pereira
• Genetic based transient identification system design with automatic selection of meaningful variables / C.M.N.A. Pereira and R. Schirru
• Optimization of fuel reload in a BWR nuclear reactor using a recurrent neural network / J.J. Ortiz and I. Requena
• Neural network methods for radial profile reconstruction for ZEFF from bremsstrahlung data on the Textor tokamak / G. Verdoolaege, G. Telesca and G. Van Oost
• The use of non linear partial least square methods for on-line process monitoring as an alternative to artificial neural networks / P.F. Fantoni [and others]
• Improving feedwater cross-correlation flow measurements in nuclear power plants with artifical neural networks / D. Roverso, D. Ruan and P.F. Fantoni
• Application of localized regularization methods for nuclear power plant sensor calibration monitoring / M.A. Buckner [and others].

FLINS is an acronym for Fuzzy Logic and Intelligent Technologies in Nuclear Science. FLINS 2002 is the fifth in a series of FLINS conferences and covers state-of-the-art research and development in computational intelligence for applied research in general and for nuclear science and engineering in particular. This book outlines the trends in computational intelligence in control, decision-making, and nuclear engineering, and presents the latest developments of computational intelligent systems in applied research and nuclear applications.
(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)

Annotation Intelligent Technologies including neural network, evolutionary computations, fuzzy approach and mainly hybrid approaches are very promising tools to build intelligent technologies in general. The progress of each theory or application is provided by a number of various theoretical as well as applicational experiments. Machine intelligence is the only alternative how to increase the level of technology to make technology more human-centred and more effective for society. This book includes theoretical as well as applicational papers in the field of neural networks, fuzzy systems and mainly evolutionary computations which application potential was increased by enormous progress in computer power. Hybrid technologies are still progressing and are trying to make some more applications with their ability to learn and process fuzzy information. Neurogenetic systems are very interesting approach to make systems re-configurable and on-line systems for real-world applications. The book is presenting papers from Japan, USA, Hungary, Poland, Germany, Finland, France, Slovakia, United Kingdom, Czech Republic and some other countries. This publication provides the latest state of the art in the field and could be contributed to theory and applications in the machine intelligence tools and their wide application potential in current and future technologies within the Information Society.

An overview of the theory and application of kernel classification methods. Linear classifiers in kernel spaces have emerged as a major topic within the field of machine learning. The kernel technique takes the linear classifier-a limited, but well-established and comprehensively studied model-and extends its applicability to a wide range of nonlinear pattern-recognition tasks such as natural language processing, machine vision, and biological sequence analysis. This book provides the first comprehensive overview of both the theory and algorithms of kernel classifiers, including the most recent developments. It begins by describing the major algorithmic advances: kernel perceptron learning, kernel Fisher discriminants, support vector machines, relevance vector machines, Gaussian processes, and Bayes point machines. Then follows a detailed introduction to learning theory, including VC and PAC-Bayesian theory, data-dependent structural risk minimization, and compression bounds. Throughout, the book emphasizes the interaction between theory and algorithms: how learning algorithms work and why. The book includes many examples, complete pseudo code of the algorithms presented, and an extensive source code library.
(source: Nielsen Book Data)

An overview of the theory and application of kernel classification methods. Linear classifiers in kernel spaces have emerged as a major topic within the field of machine learning. The kernel technique takes the linear classifier-a limited, but well-established and comprehensively studied model-and extends its applicability to a wide range of nonlinear pattern-recognition tasks such as natural language processing, machine vision, and biological sequence analysis. This book provides the first comprehensive overview of both the theory and algorithms of kernel classifiers, including the most recent developments. It begins by describing the major algorithmic advances: kernel perceptron learning, kernel Fisher discriminants, support vector machines, relevance vector machines, Gaussian processes, and Bayes point machines. Then follows a detailed introduction to learning theory, including VC and PAC-Bayesian theory, data-dependent structural risk minimization, and compression bounds. Throughout, the book emphasizes the interaction between theory and algorithms: how learning algorithms work and why. The book includes many examples, complete pseudo code of the algorithms presented, and an extensive source code library.
(source: Nielsen Book Data)

An overview of the theory and application of kernel classification methods. Linear classifiers in kernel spaces have emerged as a major topic within the field of machine learning. The kernel technique takes the linear classifier-a limited, but well-established and comprehensively studied model-and extends its applicability to a wide range of nonlinear pattern-recognition tasks such as natural language processing, machine vision, and biological sequence analysis. This book provides the first comprehensive overview of both the theory and algorithms of kernel classifiers, including the most recent developments. It begins by describing the major algorithmic advances: kernel perceptron learning, kernel Fisher discriminants, support vector machines, relevance vector machines, Gaussian processes, and Bayes point machines. Then follows a detailed introduction to learning theory, including VC and PAC-Bayesian theory, data-dependent structural risk minimization, and compression bounds. Throughout, the book emphasizes the interaction between theory and algorithms: how learning algorithms work and why. The book includes many examples, complete pseudo code of the algorithms presented, and an extensive source code library.
(source: Nielsen Book Data)

• Series Foreword; Preface; 1
• A Tutorial Introduction; I
• Concepts and Tools; 2
• Kernels; 3
• Risk and Loss Functions; 4
• Regularization; 5
• Elements of Statistical Learning Theory; 6
• Optimization; II
• Support Vector Machines; 7
• Pattern Recognition; 8
• Single-Class Problems: Quantile Estimation and Novelty Detection; 9
• Regression Estimation; 10
• Implementation; 11
• Incorporating Invariances; 12
• Learning Theory Revisited; III
• Kernel Methods; 13
• Designing Kernels; 14
• Kernel Feature Extraction; 15
• Kernel Fisher Discriminant; 16
• Bayesian Kernel Methods.

• 17
• Regularized Principal Manifolds18
• Pre-Images and Reduced Set Methods; A
• Addenda; B
• Mathematical Prerequisites; References; Index; Notation and Symbols.

A comprehensive introduction to Support Vector Machines and related kernel methods. In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically elegant learning machines that use a central concept of SVMs--kernels-for a number of learning tasks. Kernel machines provide a modular framework that can be adapted to different tasks and domains by the choice of the kernel function and the base algorithm. They are replacing neural networks in a variety of fields, including engineering, information retrieval, and bioinformatics. Learning with Kernels provides an introduction to SVMs and related kernel methods. Although the book begins with the basics, it also includes the latest research. It provides all of the concepts necessary to enable a reader equipped with some basic mathematical knowledge to enter the world of machine learning using theoretically well-founded yet easy-to-use kernel algorithms and to understand and apply the powerful algorithms that have been developed over the last few years.
(source: Nielsen Book Data)

Annotation. This book focuses on Least Squares Support Vector Machines (LS-SVMs) which are reformulations to standard SVMs. LS-SVMs are closely related to regularization networks and Gaussian processes but additionally emphasize and exploit primal-dual interpretations from optimization theory. The authors explain the natural links between LS-SVM classifiers and kernel Fisher discriminant analysis. Bayesian inference of LS-SVM models is discussed, together with methods for imposing spareness and employing robust statistics. The framework is further extended towards unsupervised learning by considering PCA analysis and its kernel version as a one-class modelling problem. This leads to new primal-dual support vector machine formulations for kernel PCA and kernel CCA analysis. Furthermore, LS-SVM formulations are given for recurrent networks and control. In general, support vector machines may pose heavy computational challenges for large data sets. For this purpose, a method of fixed size LS-SVM is proposed where the estimation is done in the primal space in relation to a Nystrom sampling with active selection of support vectors. The methods are illustrated with several examples.

Introducing Microsoft's flagship wireless development tool The .NET Mobile Web Developer's Guide will provide readers with a solid guide to developing mobile applications using Microsoft technologies. The focus of this book is on using ASP.NET and the .NET mobile SDK. It provides an introduction to the .NET platform and goes into moderate details on ASP.NET to allow readers to start developing ASP.NET applications. In addition, this book will give the readers the insight to use the various Microsoft technologies for developing mobile applications. This book assumes the readers have experience in developing web applications and are familiar with any one of the server-side technologies like ASP, JSP or PHP.
(source: Nielsen Book Data)