LEADER 03874nam 22006495 450 001 9910299225203321 005 20200703025829.0 010 $a3-319-15726-4 024 7 $a10.1007/978-3-319-15726-9 035 $a(CKB)3710000000412163 035 $a(EBL)2094427 035 $a(SSID)ssj0001501007 035 $a(PQKBManifestationID)11878171 035 $a(PQKBTitleCode)TC0001501007 035 $a(PQKBWorkID)11520845 035 $a(PQKB)10397003 035 $a(DE-He213)978-3-319-15726-9 035 $a(MiAaPQ)EBC2094427 035 $a(PPN)186025831 035 $a(EXLCZ)993710000000412163 100 $a20150507d2015 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aLearning with Partially Labeled and Interdependent Data$b[electronic resource] /$fby Massih-Reza Amini, Nicolas Usunier 205 $a1st ed. 2015. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2015. 215 $a1 online resource (113 p.) 300 $aDescription based upon print version of record. 311 $a3-319-15725-6 320 $aIncludes bibliographical references and index. 327 $aIntroduction -- Introduction to learning theory -- Semi-supervised learning -- Learning with interdependent data. 330 $aThis book develops two key machine learning principles: the semi-supervised paradigm and learning with interdependent data. It reveals new applications, primarily web related, that transgress the classical machine learning framework through learning with interdependent data. The book traces how the semi-supervised paradigm and the learning to rank paradigm emerged from new web applications, leading to a massive production of heterogeneous textual data. It explains how semi-supervised learning techniques are widely used, but only allow a limited analysis of the information content and thus do not meet the demands of many web-related tasks. Later chapters deal with the development of learning methods for ranking entities in a large collection with respect to precise information needed. In some cases, learning a ranking function can be reduced to learning a classification function over the pairs of examples. The book proves that this task can be efficiently tackled in a new framework: learning with interdependent data. Researchers and professionals in machine learning will find these new perspectives and solutions valuable. Learning with Partially Labeled and Interdependent Data is also useful for advanced-level students of computer science, particularly those focused on statistics and learning. 606 $aArtificial intelligence 606 $aData mining 606 $aStatistics  606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030 606 $aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences$3https://scigraph.springernature.com/ontologies/product-market-codes/S17020 615 0$aArtificial intelligence. 615 0$aData mining. 615 0$aStatistics . 615 14$aArtificial Intelligence. 615 24$aData Mining and Knowledge Discovery. 615 24$aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. 676 $a004 676 $a006.3 676 $a006.312 676 $a519.5 700 $aAmini$b Massih-Reza$4aut$4http://id.loc.gov/vocabulary/relators/aut$01060956 702 $aUsunier$b Nicolas$4aut$4http://id.loc.gov/vocabulary/relators/aut 906 $aBOOK 912 $a9910299225203321 996 $aLearning with Partially Labeled and Interdependent Data$92516470 997 $aUNINA