000 | 03234nam a22005055i 4500 | ||
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001 | 978-3-540-34170-3 | ||
003 | DE-He213 | ||
005 | 20161121231120.0 | ||
007 | cr nn 008mamaa | ||
008 | 100301s2006 gw | s |||| 0|eng d | ||
020 |
_a9783540341703 _9978-3-540-34170-3 |
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024 | 7 |
_a10.1007/978-3-540-34170-3 _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 |
_aKaburlasos, Vassilis G. _eauthor. |
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245 | 1 | 0 |
_aTowards a Unified Modeling and Knowledge-Representation based on Lattice Theory _h[electronic resource] : _bComputational Intelligence and Soft Computing Applications / _cby Vassilis G. Kaburlasos. |
264 | 1 |
_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c2006. |
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300 |
_aXXII, 245 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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505 | 0 | _aThe Context -- Origins in Context -- Relevant Literature Review -- Theory and Algorithms -- Novel Mathematical Background -- Real-World Grounding -- Knowledge Representation -- The Modeling Problem and its Formulation -- Algorithms for Clustering, Classification, and Regression -- Applications and Comparisons -- Numeric Data Applications -- Nonnumeric Data Applications -- Connections with Established Paradigms -- Conclusion -- Implementation Issues -- Discussion. | |
520 | _aBy ‘model’ we mean a mathematical description of a world aspect. With the proliferation of computers a variety of modeling paradigms emerged under computational intelligence and soft computing. An advancing technology is currently fragmented due, as well, to the need to cope with different types of data in different application domains. This research monograph proposes a unified, cross-fertilizing approach for knowledge-representation and modeling based on lattice theory. The emphasis is on clustering, classification, and regression applications. It is shown how rigorous analysis and design can be pursued in soft computing using conventional (hard computing) methods. Moreover, non-Turing computation can be pursued. The material here is multi-disciplinary based on our on-going research published in major scientific journals and conferences. Experimental results by various algorithms are demonstrated extensively. Relevant work by other authors is also presented both extensively and comparatively. | ||
650 | 0 | _aComputer science. | |
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 | _aArtificial Intelligence (incl. Robotics). |
650 | 2 | 4 | _aAppl.Mathematics/Computational Methods of Engineering. |
650 | 2 | 4 | _aApplications of Mathematics. |
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer eBooks | |
776 | 0 | 8 |
_iPrinted edition: _z9783540341697 |
856 | 4 | 0 | _uhttp://dx.doi.org/10.1007/978-3-540-34170-3 |
912 | _aZDB-2-ENG | ||
950 | _aEngineering (Springer-11647) | ||
999 |
_c508802 _d508802 |