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This important text and reference for researchers and students in machine learning, game theory, statistics and information theory offers a comprehensive treatment of the problem of predicting individual sequences. Unlike standard statistical approaches to forecasting, prediction of individual sequences does not impose any probabilistic assumption on the data-generating mechanism. Yet, prediction algorithms can be constructed that work well for all possible sequences, in the sense that their performance is always nearly as good as the best forecasting strategy in a given reference class. The central theme is the model of prediction using expert advice, a general framework within which many related problems can be cast and discussed. Repeated game playing, adaptive data compression, sequential investment in the stock market, sequential pattern analysis, and several other problems are viewed as instances of the experts' framework and analyzed from a common nonstochastic standpoint that often reveals new and intriguing connections.

Game theory. --- Machine learning. --- Computer algorithms. --- Théorie des jeux --- Apprentissage automatique --- Algorithmes --- Théorie des jeux --- Algorithms --- Learning, Machine --- Artificial intelligence --- Machine theory --- Games, Theory of --- Theory of games --- Mathematical models --- Mathematics

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A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines. 

Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics. The book deals with the supervised-learning problem for both regression and classification, and includes detailed algorithms. A wide variety of covariance (kernel) functions are presented and their properties discussed. Model selection is discussed both from a Bayesian and a classical perspective. Many connections to other well-known techniques from machine learning and statistics are discussed, including support-vector machines, neural networks, splines, regularization networks, relevance vector machines and others. Theoretical issues including learning curves and the PAC-Bayesian framework are treated, and several approximation methods for learning with large datasets are discussed. The book contains illustrative examples and exercises, and code and datasets are available on the Web. Appendixes provide mathematical background and a discussion of Gaussian Markov processes.

Computer assisted instruction --- Stochastic processes --- Gaussian processes --- Machine learning --- Data processing --- Mathematical models --- 681.3*I26 --- Learning: analogies; concept learning; induction; knowledge acquisition; language acquisition; parameter learning (Artificial intelligence)--See also {681.3*K32} --- Data processing. --- Mathematical models. --- 681.3*I26 Learning: analogies; concept learning; induction; knowledge acquisition; language acquisition; parameter learning (Artificial intelligence)--See also {681.3*K32} --- Learning, Machine --- Artificial intelligence --- Machine theory --- Distribution (Probability theory) --- machine learning --- statistiek --- Bayesian statistics --- Gauss, Carl Friedrich --- Aprenentatge automàtic --- Processos gaussians --- Models matemàtics --- Informàtica --- Distribució (Teoria de la probabilitat) --- Processos estocàstics --- Intel·ligència artificial --- Màquines, Teoria de --- Mathematics --- Physical Sciences & Mathematics --- Mathematical Statistics --- Gaussian processes - Data processing --- Machine learning - Mathematical models

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Machine learning is currently one of the most rapidly growing areas of research in computer science. In compiling this volume we have brought together contributions from some of the most prestigious researchers in this field. This book covers the three main learning systems; symbolic learning, neural networks and genetic algorithms as well as providing a tutorial on learning casual influences. Each of the nine chapters is self-contained. Both theoreticians and application scientists/engineers in the broad area of artificial intelligence will find this volume valuable. It also provides a useful sourcebook for Postgraduate since it shows the direction of current research.

Machine learning. --- Artificial intelligence. --- Apprentissage automatique --- Intelligence artificielle --- Engineering. --- Engineering mathematics. --- Appl.Mathematics/Computational Methods of Engineering. --- Artificial Intelligence (incl. Robotics). --- Machine learning --- Artificial intelligence --- Engineering & Applied Sciences --- Civil & Environmental Engineering --- Applied Mathematics --- Computer Science --- Civil Engineering --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Learning, Machine --- Applied mathematics. --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Mathematical and Computational Engineering. --- Artificial Intelligence. --- Engineering --- Engineering analysis --- Mathematical analysis --- Mathematics

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Recently, increasing interest has been shown in applying the concept of Pareto-optimality to machine learning, particularly inspired by the successful developments in evolutionary multi-objective optimization. It has been shown that the multi-objective approach to machine learning is particularly successful to improve the performance of the traditional single objective machine learning methods, to generate highly diverse multiple Pareto-optimal models for constructing ensembles models and, and to achieve a desired trade-off between accuracy and interpretability of neural networks or fuzzy systems. This monograph presents a selected collection of research work on multi-objective approach to machine learning, including multi-objective feature selection, multi-objective model selection in training multi-layer perceptrons, radial-basis-function networks, support vector machines, decision trees, and intelligent systems.

Machine learning --- Artificial intelligence --- Apprentissage automatique --- Intelligence artificielle --- Engineering. --- Artificial intelligence. --- Physics. --- Engineering mathematics. --- Appl.Mathematics/Computational Methods of Engineering. --- Artificial Intelligence (incl. Robotics). --- Complexity. --- Computer Science --- Applied Mathematics --- Civil Engineering --- Civil & Environmental Engineering --- Engineering & Applied Sciences --- Machine learning. --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Learning, Machine --- Statistical physics. --- Dynamical systems. --- Applied mathematics. --- Statistical Physics, Dynamical Systems and Complexity. --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Engineering --- Engineering analysis --- Mathematical analysis --- Dynamical systems --- Kinetics --- Mathematics --- Mechanics, Analytic --- Force and energy --- Mechanics --- Physics --- Statics --- Mathematical statistics --- Construction --- Industrial arts --- Technology --- Statistical methods --- Mathematical and Computational Engineering. --- Artificial Intelligence. --- Complex Systems. --- Statistical Physics and Dynamical Systems.

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This book offers a comprehensive introduction to learning classifier systems (LCS) – or more generally, rule-based evolutionary online learning systems. LCSs learn interactively – much like a neural network – but with an increased adaptivity and flexibility. This book provides the necessary background knowledge on problem types, genetic algorithms, and reinforcement learning as well as a principled, modular analysis approach to understand, analyze, and design LCSs. The analysis is exemplarily carried through on the XCS classifier system – the currently most prominent system in LCS research. Several enhancements are introduced to XCS and evaluated. An application suite is provided including classification, reinforcement learning and data-mining problems. Reconsidering John Holland’s original vision, the book finally discusses the current potentials of LCSs for successful applications in cognitive science and related areas.

Machine learning. --- Apprentissage automatique --- Engineering. --- Neurosciences. --- Artificial intelligence. --- Mathematics. --- Engineering mathematics. --- Appl.Mathematics/Computational Methods of Engineering. --- Artificial Intelligence (incl. Robotics). --- Applications of Mathematics. --- Applied Mathematics --- Civil Engineering --- Computer Science --- Engineering & Applied Sciences --- Civil & Environmental Engineering --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Learning, Machine --- Computer science. --- Computers. --- Applied mathematics. --- Computer Science. --- Theory of Computation. --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Artificial intelligence --- Information theory. --- Mathematical and Computational Engineering. --- Artificial Intelligence. --- Math --- Science --- Neural sciences --- Neurological sciences --- Neuroscience --- Medical sciences --- Nervous system --- Engineering --- Engineering analysis --- Mathematical analysis --- Communication theory --- Communication --- Cybernetics --- Mathematics --- Automatic computers --- Automatic data processors --- Computer hardware --- Computing machines (Computers) --- Electronic calculating-machines --- Electronic computers --- Hardware, Computer --- Computer systems --- Calculators --- Cyberspace