Design And Analysis Of Learning Classifier Systems
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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.
Design And Analysis Of Learning Classifier Systems
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Author : Jan Drugowitsch
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-05-30
Design And Analysis Of Learning Classifier Systems written by Jan Drugowitsch 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 2008-05-30 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.
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.
Learning Classifier Systems
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Author : Pier L. Lanzi
language : en
Publisher: Springer
Release Date : 2003-06-26
Learning Classifier Systems written by Pier L. Lanzi and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-06-26 with Computers categories.
Learning Classifier Systems (LCS) are a machine learning paradigm introduced by John Holland in 1976. They are rule-based systems in which learning is viewed as a process of ongoing adaptation to a partially unknown environment through genetic algorithms and temporal difference learning. This book provides a unique survey of the current state of the art of LCS and highlights some of the most promising research directions. The first part presents various views of leading people on what learning classifier systems are. The second part is devoted to advanced topics of current interest, including alternative representations, methods for evaluating rule utility, and extensions to existing classifier system models. The final part is dedicated to promising applications in areas like data mining, medical data analysis, economic trading agents, aircraft maneuvering, and autonomous robotics. An appendix comprising 467 entries provides a comprehensive LCS bibliography.
Multiagent Learning Classifier System And Its Application To Hot Strip Mills
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Author :
language : en
Publisher: Elias Hasnat
Release Date :
Multiagent Learning Classifier System And Its Application To Hot Strip Mills written by and has been published by Elias Hasnat this book supported file pdf, txt, epub, kindle and other format this book has been release on with Technology & Engineering categories.
Learning Classifier Systems
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Author : Pier Luca Lanzi
language : en
Publisher: Springer Science & Business Media
Release Date : 2003-11-24
Learning Classifier Systems written by Pier Luca Lanzi 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 2003-11-24 with Computers categories.
This book constitutes the refereed proceedings of the 5th International Workshop on Learning Classifier Systems, IWLCS 2003, held in Granada, Spain in September 2003 in conjunction with PPSN VII. The 10 revised full papers presented together with a comprehensive bibliography on learning classifier systems were carefully reviewed and selected during two rounds of refereeing and improvement. All relevant issues in the area are addressed.
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.
Strength Or Accuracy Credit Assignment In Learning Classifier Systems
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Author : Tim Kovacs
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Strength Or Accuracy Credit Assignment In Learning Classifier Systems written by Tim Kovacs 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 2012-12-06 with Computers categories.
Classifier systems are an intriguing approach to a broad range of machine learning problems, based on automated generation and evaluation of condi tion/action rules. Inreinforcement learning tasks they simultaneously address the two major problems of learning a policy and generalising over it (and re lated objects, such as value functions). Despite over 20 years of research, however, classifier systems have met with mixed success, for reasons which were often unclear. Finally, in 1995 Stewart Wilson claimed a long-awaited breakthrough with his XCS system, which differs from earlier classifier sys tems in a number of respects, the most significant of which is the way in which it calculates the value of rules for use by the rule generation system. Specifically, XCS (like most classifiersystems) employs a genetic algorithm for rule generation, and the way in whichit calculates rule fitness differsfrom earlier systems. Wilson described XCS as an accuracy-based classifiersystem and earlier systems as strength-based. The two differin that in strength-based systems the fitness of a rule is proportional to the return (reward/payoff) it receives, whereas in XCS it is a function of the accuracy with which return is predicted. The difference is thus one of credit assignment, that is, of how a rule's contribution to the system's performance is estimated. XCS is a Q learning system; in fact, it is a proper generalisation of tabular Q-learning, in which rules aggregate states and actions. In XCS, as in other Q-learners, Q-valuesare used to weightaction selection.
Genetic Programming
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Author : Leonardo Vanneschi
language : en
Publisher: Springer
Release Date : 2009-04-10
Genetic Programming written by Leonardo Vanneschi and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-04-10 with Computers categories.
This book constitutes the refereed proceedings of the 11th European Conference on Genetic Programming, EuroGP 2009, held in Tübingen, Germany, in April 2009 colocated with the Evo* 2009 events. The 21 revised plenary papers and 9 revised poster papers were carefully reviewed and selected from a total of 57 submissions. A great variety of topics are presented reflecting the current state of research in the field of genetic programming, including the latest work on representations, theory, operators and analysis, feature selection, generalisation, coevolution and numerous applications.