LEADER 00812nam0-22002531i-450 001 990006046630403321 005 20230112102518.0 100 $a19980601d1969----km-y0itay50------ba 101 1 $ager 102 $aDE 105 $ayyyy ---001yy 200 1 $aEntwicklungstendenzen der strafprozessualen rechtskraftlehre$eunter besonderer berucksichtigung des auslandischen rechts$fvon K. Tiedemann. 210 $aTubingen$cMohr$d1969 215 $a48 p.$d23 cm 225 1 $aRecht und Staat...$v378 676 $a345$v19$zger 700 1$aTiedemann,$bKlaus$0226166 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990006046630403321 952 $aCOLL. 24 (378/379)$b3494*$fFGBC 959 $aFGBC 996 $aEntwicklungstendenzen der strafprozessualen rechtskraftlehre$9577588 997 $aUNINA LEADER 03132nam 2200637Ia 450 001 9910437903303321 005 20200520144314.0 010 $a9781283910699 010 $a1283910691 010 $a9781461444572 010 $a1461444578 024 7 $a10.1007/978-1-4614-4457-2 035 $a(CKB)2670000000312768 035 $a(EBL)1081845 035 $a(OCoLC)823729120 035 $a(SSID)ssj0000811031 035 $a(PQKBManifestationID)11456349 035 $a(PQKBTitleCode)TC0000811031 035 $a(PQKBWorkID)10846476 035 $a(PQKB)10601339 035 $a(DE-He213)978-1-4614-4457-2 035 $a(MiAaPQ)EBC1081845 035 $a(PPN)16830029X 035 $a(EXLCZ)992670000000312768 100 $a20121210d2013 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aGraph embedding for pattern analysis /$fYun Fu, Yunqian Ma, editors 205 $a1st ed. 2013. 210 $aNew York $cSpringer$dc2013 215 $a1 online resource (263 p.) 300 $aDescription based upon print version of record. 311 08$a9781489990624 311 08$a1489990623 311 08$a9781461444565 311 08$a146144456X 320 $aIncludes bibliographical references. 327 $aMultilevel Analysis of Attributed Graphs for Explicit Graph Embedding in Vector Spaces -- Feature Grouping and Selection over an Undirected Graph -- Median Graph Computation by Means of Graph Embedding into Vector Spaces -- Patch Alignment for Graph Embedding -- Feature Subspace Transformations for Enhancing K-Means Clustering -- Learning with ?1-Graph for High Dimensional Data Analysis -- Graph-Embedding Discriminant Analysis on Riemannian Manifolds for Visual Recognition -- A Flexible and Effective Linearization Method for Subspace Learning -- A Multi-Graph Spectral Approach for Mining Multi-Source Anomalies -- Graph Embedding for Speaker Recognition. 330 $aGraph Embedding for Pattern Analysis covers theory methods, computation, and applications widely used in statistics, machine learning, image processing, and computer vision. This book presents the latest advances in graph embedding theories, such as nonlinear manifold graph, linearization method, graph based subspace analysis, L1 graph, hypergraph, undirected graph, and graph in vector spaces. Real-world applications of these theories are spanned broadly in dimensionality reduction, subspace learning, manifold learning, clustering, classification, and feature selection. A selective group of experts contribute to different chapters of this book which provides a comprehensive perspective of this field. 606 $aPattern recognition systems 606 $aGraph theory 615 0$aPattern recognition systems. 615 0$aGraph theory. 676 $a006.3 701 $aFu$b Yun$01762514 701 $aMa$b Yunqian$01753446 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910437903303321 996 $aGraph embedding for pattern analysis$94202496 997 $aUNINA