000 02918nam a22004935i 4500
001 978-3-642-38652-7
003 DE-He213
005 20140220082912.0
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
338 _aonline resource
_bcr
_2rdacarrier
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 _aArtificial intelligence.
650 0 _aEngineering mathematics.
650 0 _aOperations research.
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
999 _c98305
_d98305