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Introduction to learning classifier systems

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Introduction to learning classifier systems/ by Ryan J. Urbanowicz, Will N. Browne.
Author:	Urbanowicz, Ryan J.
other author:	Browne, Will N.
Published:	Berlin, Heidelberg :Springer Berlin Heidelberg : : 2017.,
Description:	xiii, 123 p. :ill., digital ; : 24 cm.;
Contained By:	Springer eBooks
Subject:	Learning classifier systems. -
Online resource:	http://dx.doi.org/10.1007/978-3-662-55007-6
ISBN:	9783662550076

Introduction to learning classifier systems
Urbanowicz, Ryan J.

Introduction to learning classifier systems[electronic resource] /by Ryan J. Urbanowicz, Will N. Browne. - Berlin, Heidelberg :Springer Berlin Heidelberg :2017. - xiii, 123 p. :ill., digital ;24 cm. - SpringerBriefs in intelligent systems, artificial intelligence, multiagent systems, and cognitive robotics,2196-548X. - SpringerBriefs in intelligent systems, artificial intelligence, multiagent systems, and cognitive robotics..

LCSs in a Nutshell -- LCS Concepts -- Functional Cycle Components -- LCS Adaptability -- Applying LCSs.

This accessible introduction shows the reader how to understand, implement, adapt, and apply Learning Classifier Systems (LCSs) to interesting and difficult problems. The text builds an understanding from basic ideas and concepts. The authors first explore learning through environment interaction, and then walk through the components of LCS that form this rule-based evolutionary algorithm. The applicability and adaptability of these methods is highlighted by providing descriptions of common methodological alternatives for different components that are suited to different types of problems from data mining to autonomous robotics. The authors have also paired exercises and a simple educational LCS (eLCS) algorithm (implemented in Python) with this book. It is suitable for courses or self-study by advanced undergraduate and postgraduate students in subjects such as Computer Science, Engineering, Bioinformatics, and Cybernetics, and by researchers, data analysts, and machine learning practitioners.

ISBN: 9783662550076

Standard No.: 10.1007/978-3-662-55007-6doiSubjects--Topical Terms:

1199843
Learning classifier systems.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Introduction to learning classifier systems
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520 $a This accessible introduction shows the reader how to understand, implement, adapt, and apply Learning Classifier Systems (LCSs) to interesting and difficult problems. The text builds an understanding from basic ideas and concepts. The authors first explore learning through environment interaction, and then walk through the components of LCS that form this rule-based evolutionary algorithm. The applicability and adaptability of these methods is highlighted by providing descriptions of common methodological alternatives for different components that are suited to different types of problems from data mining to autonomous robotics. The authors have also paired exercises and a simple educational LCS (eLCS) algorithm (implemented in Python) with this book. It is suitable for courses or self-study by advanced undergraduate and postgraduate students in subjects such as Computer Science, Engineering, Bioinformatics, and Cybernetics, and by researchers, data analysts, and machine learning practitioners.
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