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03908nam a22006135i 4500 |
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978-3-662-55007-6 |
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DE-He213 |
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20210619061131.0 |
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cr nn 008mamaa |
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170817s2017 gw | s |||| 0|eng d |
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|a 9783662550076
|9 978-3-662-55007-6
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|a 10.1007/978-3-662-55007-6
|2 doi
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|a Q334-342
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|a Urbanowicz, Ryan J.
|e author.
|4 aut
|4 http://id.loc.gov/vocabulary/relators/aut
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|a Introduction to Learning Classifier Systems
|h [electronic resource] /
|c by Ryan J. Urbanowicz, Will N. Browne.
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| 250 |
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|a 1st ed. 2017.
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| 264 |
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1 |
|a Berlin, Heidelberg :
|b Springer Berlin Heidelberg :
|b Imprint: Springer,
|c 2017.
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| 300 |
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|a XIII, 123 p. 27 illus., 4 illus. in color.
|b online resource.
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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|a online resource
|b cr
|2 rdacarrier
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|a text file
|b PDF
|2 rda
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| 490 |
1 |
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|a SpringerBriefs in Intelligent Systems, Artificial Intelligence, Multiagent Systems, and Cognitive Robotics,
|x 2196-548X
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| 505 |
0 |
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|a LCSs in a Nutshell -- LCS Concepts -- Functional Cycle Components -- LCS Adaptability -- Applying LCSs.
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| 520 |
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|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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| 650 |
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|a Artificial intelligence.
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| 650 |
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|a Computational intelligence.
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| 650 |
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0 |
|a Mathematical optimization.
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| 650 |
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|a Bioinformatics.
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| 650 |
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|a Control engineering.
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| 650 |
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|a Robotics.
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| 650 |
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|a Mechatronics.
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| 650 |
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|a Computers.
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| 650 |
1 |
4 |
|a Artificial Intelligence.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/I21000
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| 650 |
2 |
4 |
|a Computational Intelligence.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/T11014
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| 650 |
2 |
4 |
|a Optimization.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/M26008
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| 650 |
2 |
4 |
|a Computational Biology/Bioinformatics.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/I23050
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| 650 |
2 |
4 |
|a Control, Robotics, Mechatronics.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/T19000
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| 650 |
2 |
4 |
|a Theory of Computation.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/I16005
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| 700 |
1 |
|
|a Browne, Will N.
|e author.
|4 aut
|4 http://id.loc.gov/vocabulary/relators/aut
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| 710 |
2 |
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|a SpringerLink (Online service)
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| 773 |
0 |
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|t Springer Nature eBook
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| 776 |
0 |
8 |
|i Printed edition:
|z 9783662550069
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| 776 |
0 |
8 |
|i Printed edition:
|z 9783662550083
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| 830 |
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0 |
|a SpringerBriefs in Intelligent Systems, Artificial Intelligence, Multiagent Systems, and Cognitive Robotics,
|x 2196-548X
|
| 856 |
4 |
0 |
|u https://doi.org/10.1007/978-3-662-55007-6
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| 912 |
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|a ZDB-2-SCS
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| 912 |
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|a ZDB-2-SXCS
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| 950 |
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|a Computer Science (SpringerNature-11645)
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| 950 |
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|a Computer Science (R0) (SpringerNature-43710)
|
