000 | 03039nam a22005297a 4500 | ||
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001 | sulb-eb0025158 | ||
003 | BD-SySUS | ||
005 | 20160413122513.0 | ||
007 | cr nn 008mamaa | ||
008 | 130531s2013 gw | s |||| 0|eng d | ||
020 |
_a9783642386527 _9978-3-642-38652-7 |
||
024 | 7 |
_a10.1007/978-3-642-38652-7 _2doi |
|
050 | 4 | _aTA329-348 | |
050 | 4 | _aTA640-643 | |
072 | 7 |
_aTBJ _2bicssc |
|
072 | 7 |
_aMAT003000 _2bisacsh |
|
082 | 0 | 4 |
_a519 _223 |
100 | 1 |
_aKramer, Oliver. _eauthor. |
|
245 | 1 | 0 |
_aDimensionality Reduction with Unsupervised Nearest Neighbors _h[electronic resource] / _cby Oliver Kramer. |
264 | 1 |
_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg : _bImprint: Springer, _c2013. |
|
300 |
_aXII, 132 p. 48 illus., 45 illus. in color. _bonline resource. |
||
336 |
_atext _btxt _2rdacontent |
||
337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
||
490 | 1 |
_aIntelligent Systems Reference Library, _x1868-4394 ; _v51 |
|
505 | 0 | _aPart I Foundations -- Part II Unsupervised Nearest Neighbors -- Part III Conclusions. | |
520 | _aThis book is devoted to a novel approach for dimensionality reduction based on the famous nearest neighbor method that is a powerful classification and regression approach. It starts with an introduction to machine learning concepts and a real-world application from the energy domain. Then, unsupervised nearest neighbors (UNN) is introduced as efficient iterative method for dimensionality reduction. Various UNN models are developed step by step, reaching from a simple iterative strategy for discrete latent spaces to a stochastic kernel-based algorithm for learning submanifolds with independent parameterizations. Extensions that allow the embedding of incomplete and noisy patterns are introduced. Various optimization approaches are compared, from evolutionary to swarm-based heuristics. Experimental comparisons to related methodologies taking into account artificial test data sets and also real-world data demonstrate the behavior of UNN in practical scenarios. The book contains numerous color figures to illustrate the introduced concepts and to highlight the experimental results. . | ||
650 | 0 | _aEngineering. | |
650 | 0 | _aOperations research. | |
650 | 0 | _aDecision making. | |
650 | 0 | _aArtificial intelligence. | |
650 | 0 | _aApplied mathematics. | |
650 | 0 | _aEngineering mathematics. | |
650 | 1 | 4 | _aEngineering. |
650 | 2 | 4 | _aAppl.Mathematics/Computational Methods of Engineering. |
650 | 2 | 4 | _aArtificial Intelligence (incl. Robotics). |
650 | 2 | 4 | _aOperation Research/Decision Theory. |
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer eBooks | |
776 | 0 | 8 |
_iPrinted edition: _z9783642386510 |
830 | 0 |
_aIntelligent Systems Reference Library, _x1868-4394 ; _v51 |
|
856 | 4 | 0 | _uhttp://dx.doi.org/10.1007/978-3-642-38652-7 |
912 | _aZDB-2-ENG | ||
942 |
_2Dewey Decimal Classification _ceBooks |
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999 |
_c47250 _d47250 |