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Foundations of learning classifier systems

Foundations of learning classifier systems (Loan 2 times)

Material type
단행본
Personal Author
Bull, Larry. Kovacs, Tim 1971-
Title Statement
Foundations of learning classifier systems / Larry Bull, Tim Kovacs (eds.).
Publication, Distribution, etc
Berlin :   Springer-Verlag ,   c2005.  
Physical Medium
vi, 336 p. : ill. ; 24 cm.
Series Statement
Studies in fuzziness and soft computing , 1434-9922 ; v. 183
ISBN
3540250735 (hd.bd.)
Bibliography, Etc. Note
Includes bibliographical references.
Subject Added Entry-Topical Term
Machine learning. Genetic algorithms. Reinforcement learning (Machine learning)
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245 0 0 ▼a Foundations of learning classifier systems / ▼c Larry Bull, Tim Kovacs (eds.).
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300 ▼a vi, 336 p. : ▼b ill. ; ▼c 24 cm.
440 0 ▼a Studies in fuzziness and soft computing , ▼x 1434-9922 ; ▼v v. 183
504 ▼a Includes bibliographical references.
650 0 ▼a Machine learning.
650 0 ▼a Genetic algorithms.
650 0 ▼a Reinforcement learning (Machine learning)
700 1 ▼a Bull, Larry.
700 1 ▼a Kovacs, Tim ▼d 1971-
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Holdings Information

No. Location Call Number Accession No. Availability Due Date Make a Reservation Service
No. 1 Location Science & Engineering Library/Sci-Info(Stacks2)/ Call Number 006.31 F771 Accession No. 121121618 Availability Available Due Date Make a Reservation Service B M

Contents information

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?


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