LEADER 05018nam 22006855 450 001 9910484810403321 005 20251107152208.0 010 $a3-030-67658-7 024 7 $a10.1007/978-3-030-67658-2 035 $a(CKB)4100000011781542 035 $a(MiAaPQ)EBC6501070 035 $a(DE-He213)978-3-030-67658-2 035 $a(PPN)253858461 035 $a(EXLCZ)994100000011781542 100 $a20210224d2021 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMachine Learning and Knowledge Discovery in Databases $eEuropean Conference, ECML PKDD 2020, Ghent, Belgium, September 14?18, 2020, Proceedings, Part I /$fedited by Frank Hutter, Kristian Kersting, Jefrey Lijffijt, Isabel Valera 205 $a1st ed. 2021. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2021. 215 $a1 online resource (L, 764 p. 219 illus., 188 illus. in color.) 225 1 $aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v12457 311 08$a3-030-67657-9 327 $aPattern Mining -- clustering -- privacy and fairness -- (social) network analysis and computational social science -- dimensionality reduction and autoencoders -- domain adaptation -- sketching, sampling, and binary projections -- graphical models and causality -- (spatio-) temporal data and recurrent neural networks -- collaborative filtering and matrix completion. 330 $aThe 5-volume proceedings, LNAI 12457 until 12461 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020, which was held during September 14-18, 2020. The conference was planned to take place in Ghent, Belgium, but had to change to an online format due to the COVID-19 pandemic. The 232 full papers and 10 demo papers presented in this volume were carefully reviewed and selected for inclusion in the proceedings. The volumes are organized in topical sections as follows: Part I: Pattern Mining; clustering; privacy and fairness; (social) network analysis and computational social science; dimensionality reduction and autoencoders; domain adaptation; sketching, sampling, and binary projections; graphical models and causality; (spatio-) temporal data and recurrent neural networks; collaborative filtering and matrix completion. Part II: deep learning optimization and theory;active learning; adversarial learning; federated learning; Kernel methods and online learning; partial label learning; reinforcement learning; transfer and multi-task learning; Bayesian optimization and few-shot learning. Part III: Combinatorial optimization; large-scale optimization and differential privacy; boosting and ensemble methods; Bayesian methods; architecture of neural networks; graph neural networks; Gaussian processes; computer vision and image processing; natural language processing; bioinformatics. Part IV: applied data science: recommendation; applied data science: anomaly detection; applied data science: Web mining; applied data science: transportation; applied data science: activity recognition; applied data science: hardware and manufacturing; applied data science: spatiotemporal data. Part V: applied data science: social good; applied data science: healthcare; applied data science: e-commerce and finance; applied data science: computational social science; applied data science: sports; demo track. . 410 0$aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v12457 606 $aData mining 606 $aData structures (Computer science) 606 $aInformation theory 606 $aMachine learning 606 $aSocial sciences$xData processing 606 $aComputer vision 606 $aData Mining and Knowledge Discovery 606 $aData Structures and Information Theory 606 $aMachine Learning 606 $aComputer Application in Social and Behavioral Sciences 606 $aComputer Vision 615 0$aData mining. 615 0$aData structures (Computer science). 615 0$aInformation theory. 615 0$aMachine learning. 615 0$aSocial sciences$xData processing. 615 0$aComputer vision. 615 14$aData Mining and Knowledge Discovery. 615 24$aData Structures and Information Theory. 615 24$aMachine Learning. 615 24$aComputer Application in Social and Behavioral Sciences. 615 24$aComputer Vision. 676 $a006.312 702 $aHutter$b Frank$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aKersting$b Kristian$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aLijffijt$b Jefrey$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aValera$b Isabel$4edt$4http://id.loc.gov/vocabulary/relators/edt 906 $aBOOK 912 $a9910484810403321 996 $aMachine Learning and Knowledge Discovery in Databases$93568347 997 $aUNINA