001480959 000__ 04534cam\\22006857i\4500
001480959 001__ 1480959
001480959 003__ OCoLC
001480959 005__ 20231031003316.0
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001480959 007__ cr\un\nnnunnun
001480959 008__ 230920s2023\\\\sz\a\\\\o\\\\\101\0\eng\d
001480959 020__ $$a9783031434129$$q(electronic bk.)
001480959 020__ $$a3031434129$$q(electronic bk.)
001480959 020__ $$z9783031434112
001480959 0247_ $$a10.1007/978-3-031-43412-9$$2doi
001480959 035__ $$aSP(OCoLC)1398310398
001480959 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dOCLCO$$dWSU
001480959 049__ $$aISEA
001480959 050_4 $$aQ325.5
001480959 08204 $$a006.3/1$$223/eng/20230920
001480959 1112_ $$aECML PKDD (Conference)$$d(2023 :$$cTurin, Italy)
001480959 24510 $$aMachine learning and knowledge discovery in databases :$$bResearch track : European Conference, ECML PKDD 2023, Turin, Italy, September 18-22, 2023, Proceedings.$$nPart I /$$cDanai Koutra, Claudia Plant, Manuel Gomez Rodriguez, Elena Baralis, Francesco Bonchi, editors.
001480959 2463_ $$aECML PKDD 2023
001480959 2463_ $$aResearch track
001480959 264_1 $$aCham :$$bSpringer,$$c2023.
001480959 300__ $$a1 online resource (lv, 761 pages) :$$billustrations (some color).
001480959 336__ $$atext$$btxt$$2rdacontent
001480959 337__ $$acomputer$$bc$$2rdamedia
001480959 338__ $$aonline resource$$bcr$$2rdacarrier
001480959 4901_ $$aLecture notes in artificial intelligence
001480959 4901_ $$aLecture notes in computer science ;$$v14169
001480959 4901_ $$aLNCS sublibrary, SL 7, Artificial intelligence
001480959 500__ $$aIncludes author index.
001480959 5050_ $$aActive Learning -- Adversarial Machine Learning -- Anomaly Detection -- Applications -- Bayesian Methods -- Causality -- Clustering.
001480959 506__ $$aAccess limited to authorized users.
001480959 520__ $$aThe multi-volume set LNAI 14169 until 14175 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2023, which took place in Turin, Italy, in September 2023. The 196 papers were selected from the 829 submissions for the Research Track, and 58 papers were selected from the 239 submissions for the Applied Data Science Track. The volumes are organized in topical sections as follows: Part I: Active Learning; Adversarial Machine Learning; Anomaly Detection; Applications; Bayesian Methods; Causality; Clustering. Part II: Computer Vision; Deep Learning; Fairness; Federated Learning; Few-shot learning; Generative Models; Graph Contrastive Learning. Part III: Graph Neural Networks; Graphs; Interpretability; Knowledge Graphs; Large-scale Learning. Part IV: Natural Language Processing; Neuro/Symbolic Learning; Optimization; Recommender Systems; Reinforcement Learning; Representation Learning. Part V: Robustness; Time Series; Transfer and Multitask Learning. Part VI: Applied Machine Learning; Computational Social Sciences; Finance; Hardware and Systems; Healthcare & Bioinformatics; Human-Computer Interaction; Recommendation and Information Retrieval. Part VII: Sustainability, Climate, and Environment.- Transportation & Urban Planning.- Demo.
001480959 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed September 20, 2023).
001480959 650_6 $$aApprentissage automatique$$vCongrès.
001480959 650_6 $$aExploration de données (Informatique)$$vCongrès.
001480959 650_0 $$aMachine learning$$vCongresses.$$vCongresses$$0(DLC)sh2008107143
001480959 650_0 $$aData mining$$vCongresses.$$vCongresses$$0(DLC)sh2008102035
001480959 650_0 $$aDatabases$$vCongresses.$$0(DLC)sh2016002790
001480959 655_7 $$aConference papers and proceedings.$$2lcgft
001480959 655_0 $$aElectronic books.
001480959 7001_ $$aKoutra, Danai,$$eeditor.$$1https://orcid.org/0000-0002-3206-8179
001480959 7001_ $$aPlant, Claudia,$$eeditor.$$1https://orcid.org/0000-0001-5274-8123
001480959 7001_ $$aGomez Rodriguez, Manuel,$$eeditor.$$0(orcid)0000-0003-3930-1161$$1https://orcid.org/0000-0003-3930-1161
001480959 7001_ $$aBaralis, Elena,$$eeditor.$$0(orcid)0000-0001-9231-467X$$1https://orcid.org/0000-0001-9231-467X
001480959 7001_ $$aBonchi, Francesco,$$eeditor.$$1https://orcid.org/0000-0001-9464-8315
001480959 830_0 $$aLecture notes in computer science.$$pLecture notes in artificial intelligence.
001480959 830_0 $$aLecture notes in computer science ;$$v14169.
001480959 830_0 $$aLNCS sublibrary.$$nSL 7,$$pArtificial intelligence.
001480959 852__ $$bebk
001480959 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-43412-9$$zOnline Access$$91397441.1
001480959 909CO $$ooai:library.usi.edu:1480959$$pGLOBAL_SET
001480959 980__ $$aBIB
001480959 980__ $$aEBOOK
001480959 982__ $$aEbook
001480959 983__ $$aOnline
001480959 994__ $$a92$$bISE