Cantitate/Preț
Produs

Advances in Learning Classifier Systems

Editat de Pier L. Lanzi, Wolfgang Stolzmann, Stewart W. Wilson
en Limba Engleză Paperback – 29 aug 2001
Learning classi er systems are rule-based systems that exploit evolutionary c- putation and reinforcement learning to solve di cult problems. They were - troduced in 1978 by John H. Holland, the father of genetic algorithms, and since then they have been applied to domains as diverse as autonomous robotics, trading agents, and data mining. At the Second International Workshop on Learning Classi er Systems (IWLCS 99), held July 13, 1999, in Orlando, Florida, active researchers reported on the then current state of learning classi er system research and highlighted some of the most promising research directions. The most interesting contri- tions to the meeting are included in the book Learning Classi er Systems: From Foundations to Applications, published as LNAI 1813 by Springer-Verlag. The following year, the Third International Workshop on Learning Classi er Systems (IWLCS 2000), held September 15{16 in Paris, gave participants the opportunity to discuss further advances in learning classi er systems. We have included in this volume revised and extended versions of thirteen of the papers presented at the workshop.
Citește tot Restrânge

Toate formatele și edițiile

Toate formatele și edițiile Preț Express
Paperback (2) 31928 lei  6-8 săpt.
  Springer – 12 iun 2002 31928 lei  6-8 săpt.
  Springer – 29 aug 2001 32156 lei  6-8 săpt.

Preț: 32156 lei

Preț vechi: 40194 lei
-20%

Puncte Express: 482

Preț estimativ în valută:
5682 6549$ 4961£

Carte tipărită la comandă

Livrare economică 16-30 mai


Specificații

ISBN-13: 9783540424376
ISBN-10: 3540424377
Pagini: 288
Ilustrații: VIII, 280 p.
Dimensiuni: 155 x 235 x 16 mm
Greutate: 0.44 kg
Ediția:2001
Editura: Springer
Locul publicării:Berlin, Heidelberg, Germany

Public țintă

Research

Cuprins

Theory.- An Artificial Economy of Post Production Systems.- Simple Markov Models of the Genetic Algorithm in Classifier Systems: Accuracy-Based Fitness.- Simple Markov Models of the Genetic Algorithm in Classifier Systems: Multi-step Tasks.- Probability-Enhanced Predictions in the Anticipatory Classifier System.- YACS: Combining Dynamic Programming with Generalization in Classifier Systems.- A Self-Adaptive Classifier System.- What Makes a Problem Hard for XCS?.- Applications.- Applying a Learning Classifier System to Mining Explanatory and Predictive Models from a Large Clinical Database.- Strength and Money: An LCS Approach to Increasing Returns.- Using Classifier Systems as Adaptive Expert Systems for Control.- Mining Oblique Data with XCS.- Advanced Architectures.- A Study on the Evolution of Learning Classifier Systems.- Learning Classifier Systems Meet Multiagent Environments.- The Bibliography.- A Bigger Learning Classifier Systems Bibliography.- An Algorithmic Description of XCS.

Caracteristici

Includes supplementary material: sn.pub/extras