| 書(shū)目名稱 | Learning Classifier Systems |
| 副標(biāo)題 | 5th International Wo |
| 編輯 | Pier Luca Lanzi,Wolfgang Stolzmann,Stewart W. Wils |
| 視頻video | http://file.papertrans.cn/583/582706/582706.mp4 |
| 概述 | Includes supplementary material: |
| 叢書(shū)名稱 | Lecture Notes in Computer Science |
| 圖書(shū)封面 |  |
| 描述 | The 5th International Workshop on Learning Classi?er Systems (IWLCS2002) was held September 7–8, 2002, in Granada, Spain, during the 7th International Conference on Parallel Problem Solving from Nature (PPSN VII). We have included in this volume revised and extended versions of the papers presented at the workshop. In the ?rst paper, Browne introduces a new model of learning classi?er system, iLCS, and tests it on the Wisconsin Breast Cancer classi?cation problem. Dixon et al. present an algorithm for reducing the solutions evolved by the classi?er system XCS, so as to produce a small set of readily understandable rules. Enee and Barbaroux take a close look at Pittsburgh-style classi?er systems, focusing on the multi-agent problem known as El-farol. Holmes and Bilker investigate the effect that various types of missing data have on the classi?cation performance of learning classi?er systems. The two papers by Kovacs deal with an important theoretical issue in learning classi?er systems: the use of accuracy-based ?tness as opposed to the more traditional strength-based ?tness. In the ?rst paper, Kovacs introduces a strength-based version of XCS, called SB-XCS. The original XCS and t |
| 出版日期 | Conference proceedings 2003 |
| 關(guān)鍵詞 | adaptive classifiert systems; adaptive learning; algorithmic learning; algorithms; classification; cluste |
| 版次 | 1 |
| doi | https://doi.org/10.1007/b94229 |
| isbn_softcover | 978-3-540-20544-9 |
| isbn_ebook | 978-3-540-40029-5Series ISSN 0302-9743 Series E-ISSN 1611-3349 |
| issn_series | 0302-9743 |
| copyright | Springer-Verlag Berlin Heidelberg 2003 |
|Archiver|手機(jī)版|小黑屋|
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