| 书目名称 | Rule-Based Evolutionary Online Learning Systems | | 副标题 | A Principled Approac | | 编辑 | Martin V. Butz | | 视频video | | | 概述 | Provides a comprehensive introduction to Learning Classifiers Systems.Principle approach to understand, analyze, and design Learning Classifier Systems.Includes supplementary material: | | 丛书名称 | Studies in Fuzziness and Soft Computing | | 图书封面 |  | | 描述 | 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 approac | | 出版日期 | Book 2006 | | 关键词 | Artificial Intelligence / Machine Learning; Cognitive Science; Genetic Algorithms; Genetics-Based Machi | | 版次 | 1 | | doi | https://doi.org/10.1007/b104669 | | isbn_softcover | 978-3-642-06477-7 | | isbn_ebook | 978-3-540-31231-4Series ISSN 1434-9922 Series E-ISSN 1860-0808 | | issn_series | 1434-9922 | | copyright | Springer-Verlag Berlin Heidelberg 2006 |
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发表于 2025-3-21 16:52:17
