E Book

Foundations of Learning Classifier Systems

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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?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.

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Ketersediaan

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Informasi Detail

Judul Seri

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No. Panggil

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Penerbit

Berlin, Heidelberg : Springer., 2005

Deskripsi Fisik

VI, 336 p.online resource.

Bahasa

English

ISBN/ISSN

9783540323969

Klasifikasi

519

Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

1st ed.

Subjek

Artificial intelligence.
Bioinformatics.
Applied mathematics.
Engineering mathematics.
Mathematical and Computational Engineering.
Applications of Mathematics.

Info Detail Spesifik

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Pernyataan Tanggungjawab

Larry Bull, Tim Kovacs.

Versi lain/terkait

Lampiran Berkas

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