Cantitate/Preț
Produs
Total produse
Vezi coșul
Foundations of Learning Classifier Systems de Larry Bull

Foundations of Learning Classifier Systems: Studies in Fuzziness and Soft Computing, cartea 183

Editat de Larry Bull, Tim Kovacs
en Limba Engleză Paperback – 25 noi 2010
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.
Citește tot Restrânge

Din seria Studies in Fuzziness and Soft Computing

Preț: 91421 lei

Preț vechi: 111488 lei
-18%

Puncte Express: 1371
Paperback 91421 lei
Hardback 91589 lei

Carte tipărită la comandă

Livrare economică 18 iulie-01 august

Livrare prin curier în România Termenul estimat este afișat lângă disponibilitate.
Transport gratuit pentru acest produs Plată online sau ramburs, în funcție de opțiunile comenzii.
Retur gratuit în 14 zile Comandă securizată și suport în română.
 Adaugă în coș

Wish list
Gift list
Am citit!
Adaugă în listă

Specificații

ISBN-13: 9783642064135
ISBN-10: 3642064132
Pagini: 344
Ilustrații: VI, 336 p.
Dimensiuni: 155 x 235 x 19 mm
Greutate: 0.52 kg
Ediția:Softcover reprint of hardcover 1st edition 2005
Editura: Springer
Colecția Studies in Fuzziness and Soft Computing
Seria Studies in Fuzziness and Soft Computing

Locul publicării:Berlin, Heidelberg, Germany

Public țintă

Research

Cuprins

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?

Textul de pe ultima copertă

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.

Caracteristici

Recent theoretical work in Learning Classifier Systems (LCS) Presents a coherent framework of LCS Includes a relevant historical original work by John Holland Includes supplementary material: sn.pub/extras