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Foundations of Learning Classifier Systems
This volume brings together recent theoretical work in Learning Classifier Systems (LCS), which is a Machine Learning technique combining Genetic Algorithms and Reinforcement Learning. It includes self-contained background chapters on related fields (reinforcement learning and evolutionary computati...
| Call Number: | Libro Electrónico |
|---|---|
| Corporate Author: | |
| Other Authors: | , |
| Format: | Electronic eBook |
| Language: | Inglés |
| Published: |
Berlin, Heidelberg :
Springer Berlin Heidelberg : Imprint: Springer,
2005.
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| Edition: | 1st ed. 2005. |
| Series: | Studies in Fuzziness and Soft Computing,
183 |
| Subjects: | |
| Online Access: | Texto Completo |
Table of Contents:
- Section 1 - Rule Discovery. Population Dynamics of Genetic Algorithms. Approximating Value Functions in Classifier Systems. Two Simple Learning Classifier Systems. Computational Complexity of the XCS Classifier System. An Analysis of Continuous-Valued Representations for Learning Classifier Systems
- Section 2 - Credit Assignment. Reinforcement Learning: a Brief Overview. A Mathematical Framework for Studying Learning Classifier Systems. Rule Fitness and Pathology in Learning Classifier Systems. Learning Classifier Systems: A Reinforcement Learning Perspective. Learning Classifier Systems with Convergence and Generalization
- Section 3 - Problem Characterization. On the Classification of Maze Problems. What Makes a Problem Hard?