LEADER 04422nam 22007935 450 001 9910682565803321 005 20250617170235.0 010 $a9783031264191 010 $a3031264193 024 7 $a10.1007/978-3-031-26419-1 035 $a(MiAaPQ)EBC7216769 035 $a(Au-PeEL)EBL7216769 035 $a(CKB)26271466700041 035 $a(DE-He213)978-3-031-26419-1 035 $a(PPN)269092587 035 $a(EXLCZ)9926271466700041 100 $a20230316d2023 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 $eEuropean Conference, ECML PKDD 2022, Grenoble, France, September 19?23, 2022, Proceedings, Part V /$fedited by Massih-Reza Amini, Stéphane Canu, Asja Fischer, Tias Guns, Petra Kralj Novak, Grigorios Tsoumakas 205 $a1st ed. 2023. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2023. 215 $a1 online resource (669 pages) 225 1 $aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v13717 311 08$aPrint version: Amini, Massih-Reza Machine Learning and Knowledge Discovery in Databases Cham : Springer International Publishing AG,c2023 9783031264184 327 $aSupervised learning -- Probabilistic inference -- Optimal transport -- Optimization -- Quantum, hardware -- Sustainability. 330 $aThe multi-volume set LNAI 13713 until 13718 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2022, which took place in Grenoble, France, in September 2022. The 236 full papers presented in these proceedings were carefully reviewed and selected from a total of 1060 submissions. In addition, the proceedings include 17 Demo Track contributions. The volumes are organized in topical sections as follows: Part I: Clustering and dimensionality reduction; anomaly detection; interpretability and explainability; ranking and recommender systems; transfer and multitask learning; Part II: Networks and graphs; knowledge graphs; social network analysis; graph neural networks; natural language processing and text mining; conversational systems; Part III: Deep learning; robust and adversarial machine learning; generative models; computer vision; meta-learning, neural architecture search; Part IV: Reinforcement learning; multi-agent reinforcement learning; bandits and online learning; active and semi-supervised learning; private and federated learning; . Part V: Supervised learning; probabilistic inference; optimal transport; optimization; quantum, hardware; sustainability; Part VI: Time series; financial machine learning; applications; applications: transportation; demo track. 410 0$aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v13717 606 $aArtificial intelligence 606 $aComputer science$xMathematics 606 $aComputers 606 $aComputer engineering 606 $aComputer networks 606 $aApplication software 606 $aImage processing$xDigital techniques 606 $aComputer vision 606 $aArtificial Intelligence 606 $aMathematics of Computing 606 $aComputing Milieux 606 $aComputer Engineering and Networks 606 $aComputer and Information Systems Applications 606 $aComputer Imaging, Vision, Pattern Recognition and Graphics 615 0$aArtificial intelligence. 615 0$aComputer science$xMathematics. 615 0$aComputers. 615 0$aComputer engineering. 615 0$aComputer networks. 615 0$aApplication software. 615 0$aImage processing$xDigital techniques. 615 0$aComputer vision. 615 14$aArtificial Intelligence. 615 24$aMathematics of Computing. 615 24$aComputing Milieux. 615 24$aComputer Engineering and Networks. 615 24$aComputer and Information Systems Applications. 615 24$aComputer Imaging, Vision, Pattern Recognition and Graphics. 676 $a006.31 676 $a006.31 700 $aAmini$b Massih-Reza$01060956 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910682565803321 996 $aMachine learning and knowledge discovery in databases$93406175 997 $aUNINA