Learning Classifier Systems From Foundations to Applications /
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 p...
| Συγγραφή απο Οργανισμό/Αρχή: | |
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| Άλλοι συγγραφείς: | , , |
| Μορφή: | Ηλεκτρονική πηγή Ηλ. βιβλίο |
| Γλώσσα: | English |
| Έκδοση: |
Berlin, Heidelberg :
Springer Berlin Heidelberg : Imprint: Springer,
2000.
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| Έκδοση: | 1st ed. 2000. |
| Σειρά: | Lecture Notes in Artificial Intelligence ;
1813 |
| Θέματα: | |
| Διαθέσιμο Online: | Full Text via HEAL-Link |
Πίνακας περιεχομένων:
- Basics
- What Is a Learning Classifier System?
- A Roadmap to the Last Decade of Learning Classifier System Research (From 1989 to 1999)
- State of XCS Classifier System Research
- An Introduction to Learning Fuzzy Classifier Systems
- Advanced Topics
- Fuzzy and Crisp Representations of Real-Valued Input for Learning Classifier Systems
- Do We Really Need to Estimate Rule Utilities in Classifier Systems?
- Strength or Accuracy? Fitness Calculation in Learning Classifier Systems
- Non-homogeneous Classifier Systems in a Macro-evolution Process
- An Introduction to Anticipatory Classifier Systems
- A Corporate XCS
- Get Real! XCS with Continuous-Valued Inputs
- Applications
- XCS and the Monk's Problems
- Learning Classifier Systems Applied to Knowledge Discovery in Clinical Research Databases
- An Adaptive Agent Based Economic Model
- The Fighter Aircraft LCS: A Case of Different LCS Goals and Techniques
- Latent Learning and Action Planning in Robots with Anticipatory Classifier Systems
- The Bibliography
- A Learning Classifier Systems Bibliography.
