Buch, Englisch, Band 183, 336 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 1460 g
Buch, Englisch, Band 183, 336 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 1460 g
Reihe: Studies in Fuzziness and Soft Computing
ISBN: 978-3-540-25073-9
Verlag: Springer
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.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
- Mathematik | Informatik EDV | Informatik Angewandte Informatik Bioinformatik
- Naturwissenschaften Biowissenschaften Angewandte Biologie Bioinformatik
- Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen
- Mathematik | Informatik EDV | Informatik Informatik Mathematik für Informatiker
Weitere Infos & Material
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?
