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| 005 | 20060223103127 | |
| 008 | 060223s2005 gw a b 000 0 eng d | |
| 016 | 7 | ▼a 973647477 ▼2 GyFmDB |
| 020 | ▼a 3540250735 (hd.bd.) | |
| 024 | 3 | ▼a 9783540250739 |
| 029 | 0 | ▼a OHX ▼b har050023434 |
| 040 | ▼a OHX ▼c OHX ▼d HNK ▼d BAKER ▼d 211009 | |
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| 082 | 0 4 | ▼a 006.31 ▼2 22 |
| 090 | ▼a 006.31 ▼b F771 | |
| 245 | 0 0 | ▼a Foundations of learning classifier systems / ▼c Larry Bull, Tim Kovacs (eds.). |
| 260 | ▼a Berlin : ▼b Springer-Verlag , ▼c c2005. | |
| 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- |
| 938 | ▼a Otto Harrassowitz ▼b HARR ▼n har050023434 ▼c 139.05 EUR | |
| 938 | ▼a Baker & Taylor ▼b BKTY ▼c 169.00 ▼d 169.00 ▼i 3540250735 ▼n 0006412385 ▼s active | |
| 945 | ▼a KINS | |
| 994 | ▼a C0 ▼b KUB |
소장정보
| No. | 소장처 | 청구기호 | 등록번호 | 도서상태 | 반납예정일 | 예약 | 서비스 |
|---|---|---|---|---|---|---|---|
| No. 1 | 소장처 과학도서관/Sci-Info(2층서고)/ | 청구기호 006.31 F771 | 등록번호 121121618 (2회 대출) | 도서상태 대출가능 | 반납예정일 | 예약 | 서비스 |
컨텐츠정보
책소개
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.
New feature
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.
정보제공 :
목차
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?
정보제공 :