LEADER 04618nam 22007815 450 001 996691669503316 005 20251001130526.0 010 $a3-032-06129-6 024 7 $a10.1007/978-3-032-06129-4 035 $a(MiAaPQ)EBC32326194 035 $a(Au-PeEL)EBL32326194 035 $a(CKB)41532501000041 035 $a(DE-He213)978-3-032-06129-4 035 $a(EXLCZ)9941532501000041 100 $a20251001d2026 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. Applied Data Science Track and Demo Track $eEuropean Conference, ECML PKDD 2025, Porto, Portugal, September 15?19, 2025, Proceedings, Part X /$fedited by Inês Dutra, Mykola Pechenizkiy, Paulo Cortez, Sepideh Pashami, Arian Pasquali, Nuno Moniz, 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 (880 pages) 225 1 $aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v16022 311 08$a3-032-06128-8 330 $aThis multi-volume set, LNAI 16013 to LNAI 16022, constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2025, held in Porto, Portugal, September 15?19, 2025. The 300 full papers presented here, together with 15 demo papers, were carefully reviewed and selected from 1253 submissions. The papers presented in these proceedings are from the following three conference tracks: The Research Track in Volume LNAI 16013-16020 refers about Anomaly & Outlier Detection, Bias & Fairness, Causality, Clustering, Data Challenges, Diffusion Models, Ensemble Learning, Graph Neural Networks, Graphs & Networks, Healthcare & Bioinformatics, Images & Computer Vision, Interpretability & Explainability, Large Language Models, Learning Theory, Multimodal Data, Neuro Symbolic Approaches, Optimization, Privacy & Security, Recommender Systems, Reinforcement Learning, Representation Learning, Resource Efficiency, Robustness & Uncertainty, Sequence Models, Streaming & Spatiotemporal Data, Text & Natural Language Processing, Time Series, and Transfer & Multitask Learning. The Applied Data Science Track in Volume LNAI 16020-16022 refers about Agriculture, Food and Earth Sciences, Education, Engineering and Technology, Finance, Economy, Management or Marketing, Health, Biology, Bioinformatics or Chemistry, Industry (4.0, 5.0, Manufacturing, ...), Smart Cities, Transportation and Utilities (e.g., Energy), Sports, and Web and Social Networks The Demo Track in LNAI 16022 showcased practical applications and prototypes, accepting 15 papers from a total of 30 submissions. These proceedings cover the papers accepted in the research and applied data science tracks. 410 0$aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v16022 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 $aDutra$b Ine?s$00 701 $aPechenizkiy$b Mykola$01755820 701 $aCortez$b Paulo$0524960 701 $aPashami$b Sepideh$01849936 701 $aPasquali$b Arian$01860717 701 $aMoniz$b Nuno$01460477 701 $aJorge$b Alípio M$01849904 701 $aSoares$b Carlos$0961096 701 $aAbreu$b Pedro H$01431795 701 $aGama$b Joa?o$00 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996691669503316 996 $aMachine Learning and Knowledge Discovery in Databases. Applied Data Science Track and Demo Track$94466460 997 $aUNISA