Design and Analysis of Learning Classifier Systems

A Probabilistic Approach
2008
ISBN: 978-3-540-79865-1
Verlag: Springer Berlin Heidelberg

Buch, Englisch, Band 139, 267 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 1270 g

Reihe: Studies in Computational Intelligence

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.

Zielgruppe

Research

Autoren/Hrsg.

Drugowitsch, Jan

Fachgebiete

• Technische Wissenschaften Technik Allgemein Computeranwendungen in der Technik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
• Mathematik | Informatik EDV | Informatik Professionelle Anwendung Computer-Aided Design (CAD)
• Technische Wissenschaften Technik Allgemein Mathematik für Ingenieure
• Mathematik | Informatik EDV | Informatik Angewandte Informatik Computeranwendungen in Wissenschaft & Technologie
• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik Robotik

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.