Foundations of Learning Classifier Systems
Springer Berlin (Verlag)
978-3-540-25073-9 (ISBN)
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 computation) tailored for a classifier systems audience and written by acknowledged authorities in their area - as well as a relevant historical original work by John Holland.
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?
| Erscheint lt. Verlag | 22.7.2005 |
|---|---|
| Reihe/Serie | Studies in Fuzziness and Soft Computing |
| Zusatzinfo | VI, 336 p. |
| Verlagsort | Berlin |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Gewicht | 625 g |
| Themenwelt | Mathematik / Informatik ► Mathematik ► Angewandte Mathematik |
| Technik | |
| Schlagworte | Adaptation • algorithm • algorithms • Calculus • classification • Complexity • Dynamics • Evolution • evolutionary computation • Evolutionary Computing • Genetic algorithms • Hardcover, Softcover / Technik/Allgemeines, Lexika • HC/Technik/Allgemeines, Lexika • HC/Technik/Maschinenbau, Fertigungstechnik • Klassifikation • Knowledge • learning • machine learning • Mathematica • Reinforcement Learning • Soft Computing |
| ISBN-10 | 3-540-25073-5 / 3540250735 |
| ISBN-13 | 978-3-540-25073-9 / 9783540250739 |
| Zustand | Neuware |
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