design analysis learning classifier de drugowitsch jan (21 resultados)

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Taschenbuch. Condición: Neu. Design and Analysis of Learning Classifier Systems | A Probabilistic Approach | Jan Drugowitsch | Taschenbuch | xiv | Englisch | 2010 | Springer Berlin | EAN 9783642098611 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Buch. Condición: Neu. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 284 pp. Englisch.

Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.

Buch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.

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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 284 pp. Englisch.

Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 284 pp. Englisch.

Gebunden. Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Latest research in the area of Learning Classifier SystemsPresents a probabilistic approach to Design and Analysis of Learning Classifier SystemsThis book is probably best summarized as providing a principled foundation for Learning Classi.

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Latest research in the area of Learning Classifier SystemsPresents a probabilistic approach to Design and Analysis of Learning Classifier SystemsThis book is probably best summarized as providing a principled foundation for Learning Classi.

Condición: New. Print on Demand pp. 284 49:B&W 6.14 x 9.21 in or 234 x 156 mm (Royal 8vo) Perfect Bound on White w/Gloss Lam.

Condición: New. PRINT ON DEMAND pp. 284.

Buch. Condición: Neu. Design and Analysis of Learning Classifier Systems | A Probabilistic Approach | Jan Drugowitsch | Buch | xiv | Englisch | 2008 | Springer Berlin | EAN 9783540798651 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand.

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 284 pp. Englisch.