LEADER 04490nam 22007575 450 001 996691663803316 005 20251004130433.0 010 $a3-032-05962-3 024 7 $a10.1007/978-3-032-05962-8 035 $a(MiAaPQ)EBC32327733 035 $a(Au-PeEL)EBL32327733 035 $a(CKB)41543254500041 035 $a(DE-He213)978-3-032-05962-8 035 $a(EXLCZ)9941543254500041 100 $a20251004d2026 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. Research Track $eEuropean Conference, ECML PKDD 2025, Porto, Portugal, September 15?19, 2025, Proceedings, Part I /$fedited by Rita P. Ribeiro, Bernhard Pfahringer, Nathalie Japkowicz, Pedro Larrañaga, Alípio M. Jorge, Carlos Soares, Pedro H. Abreu, João Gama 205 $a1st ed. 2026. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2026. 215 $a1 online resource (905 pages) 225 1 $aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v16013 311 08$a3-032-05961-5 330 $aThis multi-volume set, LNAI 16013 to LNAI 16022, constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2025, held in Porto, Portugal, September 15?19, 2025. The 300 full papers presented here, together with 15 demo papers, were carefully reviewed and selected from 1253 submissions. The papers presented in these proceedings are from the following three conference tracks: The Research Track in Volume LNAI 16013-16020 refers about Anomaly & Outlier Detection, Bias & Fairness, Causality, Clustering, Data Challenges, Diffusion Models, Ensemble Learning, Graph Neural Networks, Graphs & Networks, Healthcare & Bioinformatics, Images & Computer Vision, Interpretability & Explainability, Large Language Models, Learning Theory, Multimodal Data, Neuro Symbolic Approaches, Optimization, Privacy & Security, Recommender Systems, Reinforcement Learning, Representation Learning, Resource Efficiency, Robustness & Uncertainty, Sequence Models, Streaming & Spatiotemporal Data, Text & Natural Language Processing, Time Series, and Transfer & Multitask Learning. The Applied Data Science Track in Volume LNAI 16020-16022 refers about Agriculture, Food and Earth Sciences, Education, Engineering and Technology, Finance, Economy, Management or Marketing, Health, Biology, Bioinformatics or Chemistry, Industry (4.0, 5.0, Manufacturing, ...), Smart Cities, Transportation and Utilities (e.g., Energy), Sports, and Web and Social Networks The Demo Track in LNAI 16022 showcased practical applications and prototypes, accepting 15 papers from a total of 30 submissions. These proceedings cover the papers accepted in the research and applied data science tracks. 410 0$aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v16013 606 $aArtificial intelligence 606 $aComputer networks 606 $aComputers 606 $aImage processing$xDigital techniques 606 $aComputer vision 606 $aSoftware engineering 606 $aArtificial Intelligence 606 $aComputer Communication Networks 606 $aComputing Milieux 606 $aComputer Imaging, Vision, Pattern Recognition and Graphics 606 $aSoftware Engineering 615 0$aArtificial intelligence. 615 0$aComputer networks. 615 0$aComputers. 615 0$aImage processing$xDigital techniques. 615 0$aComputer vision. 615 0$aSoftware engineering. 615 14$aArtificial Intelligence. 615 24$aComputer Communication Networks. 615 24$aComputing Milieux. 615 24$aComputer Imaging, Vision, Pattern Recognition and Graphics. 615 24$aSoftware Engineering. 676 $a006.3 700 $aRibeiro$b Rita P$01431793 701 $aPfahringer$b Bernhard$01758420 701 $aJapkowicz$b Nathalie$01753142 701 $aLarrañaga$b Pedro$01849903 701 $aJorge$b Alípio M$01849904 701 $aSoares$b Carlos$0961096 701 $aAbreu$b Pedro H$01431795 701 $aGama$b João$01431794 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996691663803316 996 $aMachine Learning and Knowledge Discovery in Databases. Research Track$94442703 997 $aUNISA