Rule-Based Evolutionary Online Learning Systems : A Principled Approach to LCS Analysis and Design /
By: Butz, Martin V [author.].
Contributor(s): SpringerLink (Online service).
Material type:
BookSeries: Studies in Fuzziness and Soft Computing: 191Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2006.Description: XXI, 259 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783540312314.Subject(s): Computer science | Neurosciences | Computers | Artificial intelligence | Applied mathematics | Engineering mathematics | Computer Science | Theory of Computation | Appl.Mathematics/Computational Methods of Engineering | Artificial Intelligence (incl. Robotics) | Neurosciences | Applications of MathematicsDDC classification: 004.0151 Online resources: Click here to access online | Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
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E books
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PK Kelkar Library, IIT Kanpur | Available | EBK9009 |
Prerequisites -- Simple Learning Classifier Systems -- The XCS Classifier System -- How XCS Works: Ensuring Effective Evolutionary Pressures -- When XCS Works: Towards Computational Complexity -- Effective XCS Search: Building Block Processing -- XCS in Binary Classification Problems -- XCS in Multi-Valued Problems -- XCS in Reinforcement Learning Problems -- Facetwise LCS Design -- Towards Cognitive Learning Classifier Systems -- Summary and Conclusions.
This book offers a comprehensive introduction to learning classifier systems (LCS) – or more generally, rule-based evolutionary online learning systems. LCSs learn interactively – much like a neural network – but with an increased adaptivity and flexibility. This book provides the necessary background knowledge on problem types, genetic algorithms, and reinforcement learning as well as a principled, modular analysis approach to understand, analyze, and design LCSs. The analysis is exemplarily carried through on the XCS classifier system – the currently most prominent system in LCS research. Several enhancements are introduced to XCS and evaluated. An application suite is provided including classification, reinforcement learning and data-mining problems. Reconsidering John Holland’s original vision, the book finally discusses the current potentials of LCSs for successful applications in cognitive science and related areas.
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