Name: Learning Classifier Systems. 5th International Workshop, IWLCS 2002, Granada, Spain, September 7-8, 2002, Revised Papers (en Inglés)
SKU: 67455422
Price: 46.89 USD
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Author: Pier Luca Lanzi;Wolfgang Stolzmann;Stewart W. Wilson

portada Learning Classifier Systems. 5th International Workshop, IWLCS 2002, Granada, Spain, September 7-8, 2002, Revised Papers (en Inglés)

Learning Classifier Systems. 5th International Workshop, IWLCS 2002, Granada, Spain, September 7-8, 2002, Revised Papers (en Inglés)

Formato

Libro Físico

Editorial

Springer

Autor

Pier Luca Lanzi;Wolfgang Stolzmann;Stewart W. Wilson

Año

2014

Idioma

Inglés

N° páginas

240

Encuadernación

Tapa Blanda

Dimensiones

23.4x15.6x1.2 cm

ISBN13

9783662172568

Learning Classifier Systems. 5th International Workshop, IWLCS 2002, Granada, Spain, September 7-8, 2002, Revised Papers (en Inglés)

Pier Luca Lanzi;Wolfgang Stolzmann;Stewart W. Wilson (Autor) · Springer · Tapa Blanda

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Reseña del libro "Learning Classifier Systems. 5th International Workshop, IWLCS 2002, Granada, Spain, September 7-8, 2002, Revised Papers (en Inglés)"

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 the new SB-XCS are compared in the second paper, where - vacs discusses the different classes of solutions that XCS and SB-XCS tend to evolve.