Learning Classifier Systems : From Foundations to Applications - Pier L. Lanzi

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Learning Classifier Systems

From Foundations to Applications

By: Pier L. Lanzi (Editor), Wolfgang Stolzmann (Editor), Stewart W. Wilson (Editor)

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Paperback | 21 June 2000

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364 Pages

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22.86 x 15.24 x 1.91

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Learning Classifier Systems (LCS) are a machine learning paradigm introduced by John Holland in 1976. They are rule-based systems in which learning is viewed as a process of ongoing adaptation to a partially unknown environment through genetic algorithms and temporal difference learning. This book provides a unique survey of the current state of the art of LCS and highlights some of the most promising research directions. The first part presents various views of leading people on what learning classifier systems are. The second part is devoted to advanced topics of current interest, including alternative representations, methods for evaluating rule utility, and extensions to existing classifier system models. The final part is dedicated to promising applications in areas like data mining, medical data analysis, economic trading agents, aircraft maneuvering, and autonomous robotics. An appendix comprising 467 entries provides a comprehensive LCS bibliography.

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You Can Find This Book In

Non-Fiction Computing & I.T.Computer Science Mathematical Theory of Computation Artificial Intelligence Mathematics Mathematical Foundation Mathematical Logic

Basics
What Is a Learning Classifier System?	p. 3
A Roadmap to the Last Decade of Learning Classifier System Research	p. 33
State of XCS Classifier System Research	p. 63
An Introduction to Learning Fuzzy Classifier Systems	p. 83
Advanced Topics
Fuzzy and Crisp Representations of Real-valued Input for Learning Classifier Systems	p. 107
Do We Really Need to Estimate Rule Utilities in Classifier Systems?	p. 125
Strength or Accuracy? Fitness calculation in learning classifier systems	p. 143
Non-homogeneous Classifier Systems in a Macro-evolution Process	p. 161
An Introduction to Anticipatory Classifier Systems	p. 175
A Corporate XCS	p. 195
Get Real! XCS with Continuous-Valued Inputs	p. 209
Applications
XCS and the Monk's Problems	p. 223
Learning Classifier Systems Applied to Knowledge Discovery in Clinical Research Databases	p. 243
An Adaptive Agent Based Economic Model	p. 263
The Fighter Aircraft LCS: A Case of Different LCS Goals and Techniques	p. 283
Latent Learning and Action Planning in Robots with Anticipatory Classifier Systems	p. 301
The Bibliography
A Learning Classifier Systems Bibliography	p. 321
Author Index	p. 349
Table of Contents provided by Publisher. All Rights Reserved.

Learning Classifier Systems

From Foundations to Applications

At a Glance

Paperback

Shipping

How to return your order

Defective items

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