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020 _a9783540450276
_9978-3-540-45027-6
024 7 _a10.1007/3-540-45027-0
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
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082 0 4 _a006.3
_223
245 1 0 _aLearning Classifier Systems
_h[electronic resource] :
_bFrom Foundations to Applications /
_cedited by Pier L. Lanzi, Wolfgang Stolzmann, Stewart W. Wilson.
250 _a1st ed. 2000.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2000.
300 _aX, 354 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aLecture Notes in Artificial Intelligence,
_x2945-9141 ;
_v1813
505 0 _aBasics -- What Is a Learning Classifier System? -- A Roadmap to the Last Decade of Learning Classifier System Research (From 1989 to 1999) -- State of XCS Classifier System Research -- An Introduction to Learning Fuzzy Classifier Systems -- Advanced Topics -- Fuzzy and Crisp Representations of Real-Valued Input for Learning Classifier Systems -- Do We Really Need to Estimate Rule Utilities in Classifier Systems? -- Strength or Accuracy? Fitness Calculation in Learning Classifier Systems -- Non-homogeneous Classifier Systems in a Macro-evolution Process -- An Introduction to Anticipatory Classifier Systems -- A Corporate XCS -- Get Real! XCS with Continuous-Valued Inputs -- Applications -- XCS and the Monk’s Problems -- Learning Classifier Systems Applied to Knowledge Discovery in Clinical Research Databases -- An Adaptive Agent Based Economic Model -- The Fighter Aircraft LCS: A Case of Different LCS Goals and Techniques -- Latent Learning and Action Planning in Robots with Anticipatory Classifier Systems -- The Bibliography -- A Learning Classifier Systems Bibliography.
520 _aLearning Classifier Systems (LCS) are a machine learning paradigm introduced by John Holland in 1976. They are rule-based systems in which learning is viewed as a process of ongoing adaptation to a partially unknown environment through genetic algorithms and temporal difference learning. This book provides a unique survey of the current state of the art of LCS and highlights some of the most promising research directions. The first part presents various views of leading people on what learning classifier systems are. The second part is devoted to advanced topics of current interest, including alternative representations, methods for evaluating rule utility, and extensions to existing classifier system models. The final part is dedicated to promising applications in areas like data mining, medical data analysis, economic trading agents, aircraft maneuvering, and autonomous robotics. An appendix comprising 467 entries provides a comprehensive LCS bibliography.
650 0 _aArtificial intelligence.
650 0 _aMachine theory.
650 0 _aComputer science.
650 1 4 _aArtificial Intelligence.
650 2 4 _aFormal Languages and Automata Theory.
650 2 4 _aTheory of Computation.
700 1 _aLanzi, Pier L.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aStolzmann, Wolfgang.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aWilson, Stewart W.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783540677291
776 0 8 _iPrinted edition:
_z9783662172186
830 0 _aLecture Notes in Artificial Intelligence,
_x2945-9141 ;
_v1813
856 4 0 _uhttps://doi.org/10.1007/3-540-45027-0
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
912 _aZDB-2-LNC
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942 _cSPRINGER
999 _c188862
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