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Cover: 9783540798651 | Design and Analysis of Learning Classifier Systems | Jan Drugowitsch
Rückseite: 9783540798651 | Design and Analysis of Learning Classifier Systems | Jan Drugowitsch
Cover: 9783540798651 | Design and Analysis of Learning Classifier Systems | Jan Drugowitsch
Rückseite: 9783540798651 | Design and Analysis of Learning Classifier Systems | Jan Drugowitsch
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Design and Analysis of Learning Classifier Systems
A Probabilistic Approach
Buch von Jan Drugowitsch
Sprache: Englisch

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Beschreibung
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.
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.
Zusammenfassung

Latest research in the area of Learning Classifier Systems

Presents a probabilistic approach to Design and Analysis of Learning Classifier Systems

Inhaltsverzeichnis
Background.- A Learning Classifier Systems Model.- A Probabilistic Model for LCS.- Training the Classifiers.- Mixing Independently Trained Classifiers.- The Optimal Set of Classifiers.- An Algorithmic Description.- Towards Reinforcement Learning with LCS.- Concluding Remarks.
Details
Erscheinungsjahr: 2008
Fachbereich: Technik allgemein
Genre: Mathematik, Medizin, Naturwissenschaften, Technik
Rubrik: Naturwissenschaften & Technik
Medium: Buch
Inhalt: xiv
267 S.
ISBN-13: 9783540798651
ISBN-10: 354079865X
Sprache: Englisch
Herstellernummer: 12261819
Einband: Gebunden
Autor: Drugowitsch, Jan
Hersteller: Springer Berlin
Springer Berlin Heidelberg
Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, D-69121 Heidelberg, juergen.hartmann@springer.com
Maße: 241 x 160 x 20 mm
Von/Mit: Jan Drugowitsch
Erscheinungsdatum: 30.05.2008
Gewicht: 0,594 kg
Artikel-ID: 101811414
Zusammenfassung

Latest research in the area of Learning Classifier Systems

Presents a probabilistic approach to Design and Analysis of Learning Classifier Systems

Inhaltsverzeichnis
Background.- A Learning Classifier Systems Model.- A Probabilistic Model for LCS.- Training the Classifiers.- Mixing Independently Trained Classifiers.- The Optimal Set of Classifiers.- An Algorithmic Description.- Towards Reinforcement Learning with LCS.- Concluding Remarks.
Details
Erscheinungsjahr: 2008
Fachbereich: Technik allgemein
Genre: Mathematik, Medizin, Naturwissenschaften, Technik
Rubrik: Naturwissenschaften & Technik
Medium: Buch
Inhalt: xiv
267 S.
ISBN-13: 9783540798651
ISBN-10: 354079865X
Sprache: Englisch
Herstellernummer: 12261819
Einband: Gebunden
Autor: Drugowitsch, Jan
Hersteller: Springer Berlin
Springer Berlin Heidelberg
Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, D-69121 Heidelberg, juergen.hartmann@springer.com
Maße: 241 x 160 x 20 mm
Von/Mit: Jan Drugowitsch
Erscheinungsdatum: 30.05.2008
Gewicht: 0,594 kg
Artikel-ID: 101811414
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