LEADER 03938nam  22008055  450 
001 996465879603316
005 20230406002651.0
010   $a3-540-34138-2
024 7 $a10.1007/11752790
035   $a(CKB)1000000000232970
035   $a(SSID)ssj0000320257
035   $a(PQKBManifestationID)11238875
035   $a(PQKBTitleCode)TC0000320257
035   $a(PQKBWorkID)10348180
035   $a(PQKB)10370877
035   $a(DE-He213)978-3-540-34138-3
035   $a(MiAaPQ)EBC3068016
035   $a(PPN)12313465X
035   $a(EXLCZ)991000000000232970
100   $a20100301d2006      u|             0
101 0 $aeng
135   $aurnn|008mamaa
181   $ctxt
182   $cc
183   $acr
200 10$aSubspace, Latent Structure and Feature Selection$b[electronic resource] $eStatistical and Optimization Perspectives Workshop, SLSFS 2005 Bohinj, Slovenia, February 23-25, 2005, Revised Selected Papers /$fedited by Craig Saunders, Marko Grobelnik, Steve Gunn, John Shawe-Taylor
205   $a1st ed. 2006.
210  1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2006.
215   $a1 online resource (X, 209 p.) 
225 1 $aTheoretical Computer Science and General Issues,$x2512-2029 ;$v3940
300   $aBibliographic Level Mode of Issuance: Monograph
311   $a3-540-34137-4 
320   $aIncludes bibliographical references and author index.
327   $aInvited Contributions -- Discrete Component Analysis -- Overview and Recent Advances in Partial Least Squares -- Random Projection, Margins, Kernels, and Feature-Selection -- Some Aspects of Latent Structure Analysis -- Feature Selection for Dimensionality Reduction -- Contributed Papers -- Auxiliary Variational Information Maximization for Dimensionality Reduction -- Constructing Visual Models with a Latent Space Approach -- Is Feature Selection Still Necessary? -- Class-Specific Subspace Discriminant Analysis for High-Dimensional Data -- Incorporating Constraints and Prior Knowledge into Factorization Algorithms ? An Application to 3D Recovery -- A Simple Feature Extraction for High Dimensional Image Representations -- Identifying Feature Relevance Using a Random Forest -- Generalization Bounds for Subspace Selection and Hyperbolic PCA -- Less Biased Measurement of Feature Selection Benefits.
410  0$aTheoretical Computer Science and General Issues,$x2512-2029 ;$v3940
606   $aAlgorithms
606   $aComputer science?Mathematics
606   $aMathematical statistics
606   $aComputer science
606   $aArtificial intelligence
606   $aComputer vision
606   $aPattern recognition systems
606   $aAlgorithms
606   $aProbability and Statistics in Computer Science
606   $aTheory of Computation
606   $aArtificial Intelligence
606   $aComputer Vision
606   $aAutomated Pattern Recognition
615  0$aAlgorithms.
615  0$aComputer science?Mathematics.
615  0$aMathematical statistics.
615  0$aComputer science.
615  0$aArtificial intelligence.
615  0$aComputer vision.
615  0$aPattern recognition systems.
615 14$aAlgorithms.
615 24$aProbability and Statistics in Computer Science.
615 24$aTheory of Computation.
615 24$aArtificial Intelligence.
615 24$aComputer Vision.
615 24$aAutomated Pattern Recognition.
676   $a003/.1
702   $aSaunders$b Craig$4edt$4http://id.loc.gov/vocabulary/relators/edt
702   $aGrobelnik$b Marko$4edt$4http://id.loc.gov/vocabulary/relators/edt
702   $aGunn$b Steve$4edt$4http://id.loc.gov/vocabulary/relators/edt
702   $aShawe-Taylor$b John$4edt$4http://id.loc.gov/vocabulary/relators/edt
906   $aBOOK
912   $a996465879603316
996   $aSubspace, Latent Structure and Feature Selection$9772769
997   $aUNISA