Rule Based Evolutionary Online Learning Systems

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Rule Based Evolutionary Online Learning Systems

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Author : Martin V. Butz
language : en
Publisher: Springer
Release Date : 2006-01-04

Rule Based Evolutionary Online Learning Systems written by Martin V. Butz and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-01-04 with Computers categories.

Rule-basedevolutionaryonlinelearningsystems,oftenreferredtoasMichig- style learning classi?er systems (LCSs), were proposed nearly thirty years ago (Holland, 1976; Holland, 1977) originally calling them cognitive systems. LCSs combine the strength of reinforcement learning with the generali- tion capabilities of genetic algorithms promising a ?exible, online general- ing, solely reinforcement dependent learning system. However, despite several initial successful applications of LCSs and their interesting relations with a- mal learning and cognition, understanding of the systems remained somewhat obscured. Questions concerning learning complexity or convergence remained unanswered. Performance in di?erent problem types, problem structures, c- ceptspaces,andhypothesisspacesstayednearlyunpredictable. Thisbookhas the following three major objectives: (1) to establish a facetwise theory - proachforLCSsthatpromotessystemanalysis,understanding,anddesign;(2) to analyze, evaluate, and enhance the XCS classi?er system (Wilson, 1995) by the means of the facetwise approach establishing a fundamental XCS learning theory; (3) to identify both the major advantages of an LCS-based learning approach as well as the most promising potential application areas. Achieving these three objectives leads to a rigorous understanding of LCS functioning that enables the successful application of LCSs to diverse problem types and problem domains. The quantitative analysis of XCS shows that the inter- tive, evolutionary-based online learning mechanism works machine learning competitively yielding a low-order polynomial learning complexity. Moreover, the facetwise analysis approach facilitates the successful design of more - vanced LCSs including Holland’s originally envisioned cognitive systems. Martin V.

Learning Classifier Systems

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Author : Jaume Bacardit
language : en
Publisher: Springer
Release Date : 2010-11-26

Learning Classifier Systems written by Jaume Bacardit and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-11-26 with Computers categories.

This book constitutes the thoroughly refereed joint post-conference proceedings of two consecutive International Workshops on Learning Classifier Systems that took place in Atlanta, GA, USA in July 2008, and in Montreal, Canada, in July 2009 - all hosted by the Genetic and Evolutionary Computation Conference, GECCO. The 12 revised full papers presented were carefully reviewed and selected from the workshop contributions. The papers are organized in topical sections on LCS in general, function approximation, LCS in complex domains, and applications.

Learning Classifier Systems

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Author : Tim Kovacs
language : en
Publisher: Springer
Release Date : 2007-06-11

Learning Classifier Systems written by Tim Kovacs and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-06-11 with Computers categories.

This book constitutes the thoroughly refereed joint post-proceedings of three consecutive International Workshops on Learning Classifier Systems that took place in Chicago, IL in July 2003, in Seattle, WA in June 2004, and in Washington, DC in June 2005. Topics in the 22 revised full papers range from theoretical analysis of mechanisms to practical consideration for successful application of such techniques to everyday datamining tasks.

Multi Objective Machine Learning

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Author : Yaochu Jin
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-06-10

Multi Objective Machine Learning written by Yaochu Jin and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-06-10 with Technology & Engineering categories.

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.

Design And Analysis Of Learning Classifier Systems

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Author : Jan Drugowitsch
language : en
Publisher: Springer
Release Date : 2008-06-17

Design And Analysis Of Learning Classifier Systems written by Jan Drugowitsch and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-06-17 with Computers categories.

This book is probably best summarized as providing a principled foundation for Learning Classi?er Systems. Something is happening in LCS, and particularly XCS and its variants that clearly often produces good results. Jan Drug- itsch wishes to understand this from a broader machine learning perspective and thereby perhaps to improve the systems. His approach centers on choosing a statistical de?nition – derived from machine learning – of “a good set of cl- si?ers”, based on a model according to which such a set represents the data. For an illustration of this approach, he designs the model to be close to XCS, and tests it by evolving a set of classi?ers using that de?nition as a ?tness criterion, seeing ifthe setprovidesa goodsolutionto twodi?erent function approximation problems. It appears to, meaning that in some sense his de?nition of “good set of classi?ers” (also, in his terms, a good model structure) captures the essence, in machine learning terms, of what XCS is doing. In the process of designing the model, the author describes its components and their training in clear detail and links it to currently used LCS, giving rise to recommendations for how those LCS can directly gain from the design of the model and its probabilistic formulation. The seeming complexity of evaluating the quality ofa set ofclassi?ersis alleviatedby giving analgorithmicdescription of how to do it, which is carried out via a simple Pittsburgh-style LCS.

Reinforcement Learning

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Author : Marco Wiering
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-03-05

Reinforcement Learning written by Marco Wiering and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-03-05 with Computers categories.

Reinforcement learning encompasses both a science of adaptive behavior of rational beings in uncertain environments and a computational methodology for finding optimal behaviors for challenging problems in control, optimization and adaptive behavior of intelligent agents. As a field, reinforcement learning has progressed tremendously in the past decade. The main goal of this book is to present an up-to-date series of survey articles on the main contemporary sub-fields of reinforcement learning. This includes surveys on partially observable environments, hierarchical task decompositions, relational knowledge representation and predictive state representations. Furthermore, topics such as transfer, evolutionary methods and continuous spaces in reinforcement learning are surveyed. In addition, several chapters review reinforcement learning methods in robotics, in games, and in computational neuroscience. In total seventeen different subfields are presented by mostly young experts in those areas, and together they truly represent a state-of-the-art of current reinforcement learning research. Marco Wiering works at the artificial intelligence department of the University of Groningen in the Netherlands. He has published extensively on various reinforcement learning topics. Martijn van Otterlo works in the cognitive artificial intelligence group at the Radboud University Nijmegen in The Netherlands. He has mainly focused on expressive knowledge representation in reinforcement learning settings.

Soft Computing Models In Industrial And Environmental Applications

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Author : Václav Snášel
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-08-23

Soft Computing Models In Industrial And Environmental Applications written by Václav Snášel and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-08-23 with Technology & Engineering categories.

This volume of Advances in Intelligent and Soft Computing contains accepted papers presented at SOCO 2012, held in the beautiful and historic city of Ostrava (Czech Republic), in September 2012. Soft computing represents a collection or set of computational techniques in machine learning, computer science and some engineering disciplines, which investigate, simulate, and analyze very complex issues and phenomena. After a through peer-review process, the SOCO 2012 International Program Committee selected 75 papers which are published in these conference proceedings, and represents an acceptance rate of 38%. In this relevant edition a special emphasis was put on the organization of special sessions. Three special sessions were organized related to relevant topics as: Soft computing models for Control Theory & Applications in Electrical Engineering, Soft computing models for biomedical signals and data processing and Advanced Soft Computing Methods in Computer Vision and Data Processing. The selection of papers was extremely rigorous in order to maintain the high quality of the conference and we would like to thank the members of the Program Committees for their hard work in the reviewing process. This is a crucial process to the creation of a high standard conference and the SOCO conference would not exist without their help.

Anticipatory Behavior In Adaptive Learning Systems

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Author : Giovanni Pezzulo
language : en
Publisher: Springer Science & Business Media
Release Date : 2009-06-15

Anticipatory Behavior In Adaptive Learning Systems written by Giovanni Pezzulo and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-06-15 with Technology & Engineering categories.

Anticipatory behavior in adaptive learning systems continues attracting attention of researchers in many areas, including cognitive systems, neuroscience, psychology, and machine learning. This book constitutes the thoroughly refereed post-workshop proceedings of the 4th International Workshop on Anticipatory Behavior in Adaptive Learning Systems, ABiALS 2008, held in Munich, Germany, in June 2008, in collaboration with the six-monthly Meeting of euCognition 'The Role of Anticipation in Cognition'. The 18 revised full papers presented were carefully selected during two rounds of reviewing and improvement for inclusion in the book. The introductory chapter of this state-of-the-art survey not only provides an overview of the contributions included in this volume but also revisits the current available terminology on anticipatory behavior and relates it to the available system approaches. The papers are organized in topical sections on anticipation in psychology with focus on the ideomotor view, conceptualizations, anticipation and dynamical systems, computational modeling of psychological processes in the individual and social domains, behavioral and cognitive capabilities based on anticipation, and computational frameworks and algorithms for anticipation, and their evaluation.

Ai 2012 Advances In Artificial Intelligence

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Author : Michael Thielscher
language : en
Publisher: Springer
Release Date : 2013-02-01

Ai 2012 Advances In Artificial Intelligence written by Michael Thielscher and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-02-01 with Computers categories.

This book constitutes the refereed proceedings of the 25th Australasian Joint Conference on Artificial Intelligence, AI 2012, held in Sydney, Australia, in December 2012. The 76 revised full papers presented were carefully reviewed and selected from 196 submissions. The papers address a wide range of agents, applications, computer vision, constraints and search, game playing, information retrieval, knowledge representation, machine learning, planning and scheduling, robotics and uncertainty in AI.

Computational Issues In Fluid Construction Grammar

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Author : Luc STEELS
language : en
Publisher: Springer
Release Date : 2012-10-30

Computational Issues In Fluid Construction Grammar written by Luc STEELS and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-10-30 with Computers categories.

This state-of-the-art-survey documents the Fluid Construction Grammar (FCG), a new formalism for the representation of lexicons and grammars, which has been used in a wide range of case studies for different languages, both for studying specific grammatical phenomena and design patterns, as for investigating language learning and language evolution. The book focuses on the many complex computational issues that arise when writing challenging real world grammars and hence emphasises depth of analysis rather than broad scope. The volume contains 13 contributions organized in 5 parts from "Basic", and "Implementation", over "Case Studies", and "Formal Analysis", up to 3 papers presenting a "Conclusion".