Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track and Demo Track : European Conference, ECML PKDD 2025, Porto, Portugal, September 15–19, 2025, Proceedings, Part X / / edited by Inês Dutra, Mykola Pechenizkiy, Paulo Cortez, Sepideh Pashami, Arian Pasquali, Nuno Moniz, Alípio M. Jorge, Carlos Soares, Pedro H. Abreu, João Gama

• Autore: Dutra Inês
• Pubblicazione: Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2026
• Edizione: 1st ed. 2026.
• Descrizione fisica: 1 online resource (880 pages)
• Disciplina: 006.3
• Soggetto topico: Artificial intelligence; Computer networks; Computers; Image processing - Digital techniques; Computer vision; Software engineering; Artificial Intelligence; Computer Communication Networks; Computing Milieux; Computer Imaging, Vision, Pattern Recognition and Graphics; Software Engineering
• Altri autori: PechenizkiyMykola CortezPaulo PashamiSepideh PasqualiArian MonizNuno JorgeAlípio M SoaresCarlos AbreuPedro H GamaJoão
• Sommario/riassunto: This 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.
• Titolo autorizzato: Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track and Demo Track
• ISBN: 3-032-06129-6
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 996691669503316
• Lo trovi qui: Univ. di Salerno
• Serie: Lecture Notes in Artificial Intelligence, . 2945-9141 ; ; 16022