| 书目名称 | Learning Classifier Systems |
| 副标题 | From Foundations to |
| 编辑 | Pier Luca Lanzi,Wolfgang Stolzmann,Stewart W. Wils |
| 视频video | |
| 概述 | Includes supplementary material: |
| 丛书名称 | Lecture Notes in Computer Science |
| 图书封面 |  |
| 描述 | 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 provides a unique survey of the current state of the art of LCS and highlights some of the most promising research directions. The first part presents various views of leading people on what learning classifier systems are. The second part is devoted to advanced topics of current interest, including alternative representations, methods for evaluating rule utility, and extensions to existing classifier system models. The final part is dedicated to promising applications in areas like data mining, medical data analysis, economic trading agents, aircraft maneuvering, and autonomous robotics. An appendix comprising 467 entries provides a comprehensive LCS bibliography. |
| 出版日期 | Conference proceedings 2000 |
| 关键词 | Extension; agents; algorithmic learning; algorithms; autonomous robot; data mining; evolution; fuzzy; geneti |
| 版次 | 1 |
| doi | https://doi.org/10.1007/3-540-45027-0 |
| isbn_softcover | 978-3-540-67729-1 |
| isbn_ebook | 978-3-540-45027-6Series ISSN 0302-9743 Series E-ISSN 1611-3349 |
| issn_series | 0302-9743 |
| copyright | Springer-Verlag Berlin Heidelberg 2000 |
发表于 2025-3-21 19:31:46
