LEADER 04566nam 22007095 450 001 996418286803316 005 20200705110109.0 010 $a3-030-46133-5 024 7 $a10.1007/978-3-030-46133-1 035 $a(CKB)4100000011223260 035 $a(MiAaPQ)EBC6303635 035 $a(DE-He213)978-3-030-46133-1 035 $a(PPN)243761201 035 $a(EXLCZ)994100000011223260 100 $a20200430d2020 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMachine Learning and Knowledge Discovery in Databases$b[electronic resource] $eEuropean Conference, ECML PKDD 2019, Würzburg, Germany, September 16?20, 2019, Proceedings, Part III /$fedited by Ulf Brefeld, Elisa Fromont, Andreas Hotho, Arno Knobbe, Marloes Maathuis, Céline Robardet 205 $a1st ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource (819 pages) 225 1 $aLecture Notes in Artificial Intelligence ;$v11908 300 $aIncludes index. 311 $a3-030-46132-7 327 $aReinforcement Learning and Bandits -- Ranking -- Applied Data Science: Computer Vision and Explanation -- Applied Data Science: Healthcare -- Applied Data Science: E-commerce, Finance, and Advertising -- Applied Data Science: Rich Data -- Applied Data Science: Applications -- Demo Track. 330 $aThe three volume proceedings LNAI 11906 ? 11908 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2019, held in Würzburg, Germany, in September 2019. The total of 130 regular papers presented in these volumes was carefully reviewed and selected from 733 submissions; there are 10 papers in the demo track. The contributions were organized in topical sections named as follows: Part I: pattern mining; clustering, anomaly and outlier detection, and autoencoders; dimensionality reduction and feature selection; social networks and graphs; decision trees, interpretability, and causality; strings and streams; privacy and security; optimization. Part II: supervised learning; multi-label learning; large-scale learning; deep learning; probabilistic models; natural language processing. Part III: reinforcement learning and bandits; ranking; applied data science: computer vision and explanation; applied data science: healthcare; applied data science: e-commerce, finance, and advertising; applied data science: rich data; applied data science: applications; demo track. 410 0$aLecture Notes in Artificial Intelligence ;$v11908 606 $aArtificial intelligence 606 $aApplication software 606 $aComputers 606 $aComputer organization 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aComputer Applications$3https://scigraph.springernature.com/ontologies/product-market-codes/I23001 606 $aInformation Systems and Communication Service$3https://scigraph.springernature.com/ontologies/product-market-codes/I18008 606 $aComputing Milieux$3https://scigraph.springernature.com/ontologies/product-market-codes/I24008 606 $aComputer Systems Organization and Communication Networks$3https://scigraph.springernature.com/ontologies/product-market-codes/I13006 615 0$aArtificial intelligence. 615 0$aApplication software. 615 0$aComputers. 615 0$aComputer organization. 615 14$aArtificial Intelligence. 615 24$aComputer Applications. 615 24$aInformation Systems and Communication Service. 615 24$aComputing Milieux. 615 24$aComputer Systems Organization and Communication Networks. 676 $a006.31 702 $aBrefeld$b Ulf$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aFromont$b Elisa$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aHotho$b Andreas$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aKnobbe$b Arno$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aMaathuis$b Marloes$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aRobardet$b Céline$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996418286803316 996 $aMachine Learning and Knowledge Discovery in Databases$9773712 997 $aUNISA