LEADER 01396nam a2200289 i 4500 001 991001699609707536 008 120504s2009 ua ah 100 0 fre d 020 $a9782724704983 020 $a2724704983 035 $ab14055673-39ule_inst 040 $aBiblioteca Interfacoltà$bita 111 2 $aInternational Conference of Demotic Studies$n<9. ;$d2005 ;$cParis>$0476965 245 00$aActes du IXe Congrès International des Études Démotiques :$bParis, 31 août - 3 Septembre 2005 /$cédités par Ghislaine Widmer et Didier Devauchelle 246 13$aActes du neuvième Congrés International des Études Démotiques 260 $aLe Caire :$bInstitut Fran?cais d'Archéologie Orientale,$cc2009 300 $aXX, 387 p. :$bill., facsims. ;$c29 cm. 440 0$aBibliothèque d'étude;$v147 650 4$aLingua egiziana antica 700 1 $aWidmer, Ghislaine$eauthor$4http://id.loc.gov/vocabulary/relators/aut$0732007 700 1 $aDevauchelle, Didier 710 2 $aInstitut français d'archéologie orientale du Caire 907 $a.b14055673$b02-04-14$c04-05-12 912 $a991001699609707536 945 $aLE002 Museo Papirologico BELT Coll. BiEtud 147$g1$lle002$og$pE42.00$q-$rn$s- $t18$u0$v0$w0$x0$y.i15406611$z04-05-12 996 $aActes du IXe Congrès International des Études Démotiques$91442231 997 $aUNISALENTO 998 $ale002$b04-05-12$cm$da $e-$ffre$gua $h0$i0 LEADER 03493nam 22006135 450 001 9910736023303321 005 20230727113449.0 010 $a9783031335600 010 $a3031335600 024 7 $a10.1007/978-3-031-33560-0 035 $a(MiAaPQ)EBC30668291 035 $a(Au-PeEL)EBL30668291 035 $a(DE-He213)978-3-031-33560-0 035 $a(PPN)272251798 035 $a(CKB)27867633400041 035 $a(OCoLC)1391990152 035 $a(EXLCZ)9927867633400041 100 $a20230727d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMulti-aspect Learning $eMethods and Applications /$fby Richi Nayak, Khanh Luong 205 $a1st ed. 2023. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2023. 215 $a1 online resource (191 pages) 225 1 $aIntelligent Systems Reference Library,$x1868-4408 ;$v242 311 08$aPrint version: Nayak, Richi Multi-Aspect Learning Cham : Springer International Publishing AG,c2023 9783031335594 327 $a1 Multi-Aspect Data Learning: Overview, Challenges and Approaches -- 2 Non-negative Matrix Factorization-Based Multi-Aspect Data Clustering -- 3 NMF and Manifold Learning for Multi-Aspect Data -- 4 Subspace Learning for Multi-Aspect Data -- 5 Spectral Clustering on Multi-Aspect Data -- 6 Learning Consensus and Complementary Information for Multi-Aspect Data Clustering -- 7 Deep Learning-Based Methods for Multi-Aspect Data Clustering. 330 $aThis book offers a detailed and comprehensive analysis of multi-aspect data learning, focusing especially on representation learning approaches for unsupervised machine learning. It covers state-of-the-art representation learning techniques for clustering and their applications in various domains. This is the first book to systematically review multi-aspect data learning, incorporating a range of concepts and applications. Additionally, it is the first to comprehensively investigate manifold learning for dimensionality reduction in multi-view data learning. The book presents the latest advances in matrix factorization, subspace clustering, spectral clustering and deep learning methods, with a particular emphasis on the challenges and characteristics of multi-aspect data. Each chapter includes a thorough discussion of state-of-the-art of multi-aspect data learning methods and important research gaps. The book provides readers with the necessary foundational knowledge to apply these methods to new domains and applications, as well as inspire new research in this emerging field. 410 0$aIntelligent Systems Reference Library,$x1868-4408 ;$v242 606 $aEngineering?Data processing 606 $aComputational intelligence 606 $aMachine learning 606 $aData Engineering 606 $aComputational Intelligence 606 $aMachine Learning 615 0$aEngineering?Data processing. 615 0$aComputational intelligence. 615 0$aMachine learning. 615 14$aData Engineering. 615 24$aComputational Intelligence. 615 24$aMachine Learning. 676 $a620.00285 700 $aNayak$b Richi$01361180 701 $aLuong$b Khanh$01380287 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910736023303321 996 $aMulti-Aspect Learning$93421568 997 $aUNINA