000 | 03291nam a22005535i 4500 | ||
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001 | 978-3-540-31231-4 | ||
003 | DE-He213 | ||
005 | 20161121231117.0 | ||
007 | cr nn 008mamaa | ||
008 | 100301s2006 gw | s |||| 0|eng d | ||
020 |
_a9783540312314 _9978-3-540-31231-4 |
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024 | 7 |
_a10.1007/b104669 _2doi |
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050 | 4 | _aQA75.5-76.95 | |
072 | 7 |
_aUY _2bicssc |
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072 | 7 |
_aUYA _2bicssc |
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072 | 7 |
_aCOM014000 _2bisacsh |
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072 | 7 |
_aCOM031000 _2bisacsh |
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082 | 0 | 4 |
_a004.0151 _223 |
100 | 1 |
_aButz, Martin V. _eauthor. |
|
245 | 1 | 0 |
_aRule-Based Evolutionary Online Learning Systems _h[electronic resource] : _bA Principled Approach to LCS Analysis and Design / _cby Martin V. Butz. |
264 | 1 |
_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c2006. |
|
300 |
_aXXI, 259 p. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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490 | 1 |
_aStudies in Fuzziness and Soft Computing, _x1434-9922 ; _v191 |
|
505 | 0 | _aPrerequisites -- Simple Learning Classifier Systems -- The XCS Classifier System -- How XCS Works: Ensuring Effective Evolutionary Pressures -- When XCS Works: Towards Computational Complexity -- Effective XCS Search: Building Block Processing -- XCS in Binary Classification Problems -- XCS in Multi-Valued Problems -- XCS in Reinforcement Learning Problems -- Facetwise LCS Design -- Towards Cognitive Learning Classifier Systems -- Summary and Conclusions. | |
520 | _aThis book offers a comprehensive introduction to learning classifier systems (LCS) – or more generally, rule-based evolutionary online learning systems. LCSs learn interactively – much like a neural network – but with an increased adaptivity and flexibility. This book provides the necessary background knowledge on problem types, genetic algorithms, and reinforcement learning as well as a principled, modular analysis approach to understand, analyze, and design LCSs. The analysis is exemplarily carried through on the XCS classifier system – the currently most prominent system in LCS research. Several enhancements are introduced to XCS and evaluated. An application suite is provided including classification, reinforcement learning and data-mining problems. Reconsidering John Holland’s original vision, the book finally discusses the current potentials of LCSs for successful applications in cognitive science and related areas. | ||
650 | 0 | _aComputer science. | |
650 | 0 | _aNeurosciences. | |
650 | 0 | _aComputers. | |
650 | 0 | _aArtificial intelligence. | |
650 | 0 | _aApplied mathematics. | |
650 | 0 | _aEngineering mathematics. | |
650 | 1 | 4 | _aComputer Science. |
650 | 2 | 4 | _aTheory of Computation. |
650 | 2 | 4 | _aAppl.Mathematics/Computational Methods of Engineering. |
650 | 2 | 4 | _aArtificial Intelligence (incl. Robotics). |
650 | 2 | 4 | _aNeurosciences. |
650 | 2 | 4 | _aApplications of Mathematics. |
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer eBooks | |
776 | 0 | 8 |
_iPrinted edition: _z9783540253792 |
830 | 0 |
_aStudies in Fuzziness and Soft Computing, _x1434-9922 ; _v191 |
|
856 | 4 | 0 | _uhttp://dx.doi.org/10.1007/b104669 |
912 | _aZDB-2-ENG | ||
950 | _aEngineering (Springer-11647) | ||
999 |
_c508722 _d508722 |