國立虎尾科技大學 |

Introduction to learning classifier systems

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Introduction to learning classifier systems/ by Ryan J. Urbanowicz, Will N. Browne.
作者:	Urbanowicz, Ryan J.
其他作者:	Browne, Will N.
出版者:	Berlin, Heidelberg :Springer Berlin Heidelberg : : 2017.,
面頁冊數:	xiii, 123 p. :ill., digital ; : 24 cm.;
Contained By:	Springer eBooks
標題:	Learning classifier systems. -
電子資源:	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
LDR:02223nam a2200337 a 4500 001 923477
003 DE-He213
005 20180313162230.0
006 m d
007 cr nn 008maaau
008 190625s2017 gw s 0 eng d
020 $a 9783662550076 $q (electronic bk.)
020 $a 9783662550069 $q (paper)
024 7 $a 10.1007/978-3-662-55007-6 $2 doi
035 $a 978-3-662-55007-6
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.5
072 7 $a UYQ $2 bicssc
072 7 $a TJFM1 $2 bicssc
072 7 $a COM004000 $2 bisacsh
082 0 4 $a 006.31 $2 23
090 $a Q325.5 $b .U72 2017
100 1 $a Urbanowicz, Ryan J. $3 1199841
245 1 0 $a Introduction to learning classifier systems $h [electronic resource] / $c by Ryan J. Urbanowicz, Will N. Browne.
260 $a Berlin, Heidelberg : $c 2017. $b Springer Berlin Heidelberg : $b Imprint: Springer,
300 $a xiii, 123 p. : $b ill., digital ; $c 24 cm.
490 1 $a SpringerBriefs in intelligent systems, artificial intelligence, multiagent systems, and cognitive robotics, $x 2196-548X
505 0 $a LCSs in a Nutshell -- LCS Concepts -- Functional Cycle Components -- LCS Adaptability -- Applying LCSs.
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.
650 0 $a Learning classifier systems. $3 1199843
650 1 4 $a Computer Science. $3 593922
650 2 4 $a Artificial Intelligence (incl. Robotics) $3 593924
650 2 4 $a Computational Intelligence. $3 768837
650 2 4 $a Optimization. $3 669174
650 2 4 $a Computational Biology/Bioinformatics. $3 677363
650 2 4 $a Control, Robotics, Mechatronics. $3 768396
650 2 4 $a Theory of Computation. $3 669322
700 1 $a Browne, Will N. $3 1199842
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer eBooks
830 0 $a SpringerBriefs in intelligent systems, artificial intelligence, multiagent systems, and cognitive robotics. $3 1069261
856 4 0 $u http://dx.doi.org/10.1007/978-3-662-55007-6
950 $a Computer Science (Springer-11645)