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Record Nr. |
UNINA9910682565803321 |
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Autore |
Amini Massih-Reza |
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Titolo |
Machine Learning and Knowledge Discovery in Databases : European Conference, ECML PKDD 2022, Grenoble, France, September 19–23, 2022, Proceedings, Part V / / edited by Massih-Reza Amini, Stéphane Canu, Asja Fischer, Tias Guns, Petra Kralj Novak, Grigorios Tsoumakas |
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Pubbl/distr/stampa |
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Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 |
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ISBN |
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Edizione |
[1st ed. 2023.] |
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Descrizione fisica |
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1 online resource (669 pages) |
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Collana |
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Lecture Notes in Artificial Intelligence, , 2945-9141 ; ; 13717 |
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Disciplina |
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Soggetti |
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Artificial intelligence |
Computer science - Mathematics |
Computers |
Computer engineering |
Computer networks |
Application software |
Image processing - Digital techniques |
Computer vision |
Artificial Intelligence |
Mathematics of Computing |
Computing Milieux |
Computer Engineering and Networks |
Computer and Information Systems Applications |
Computer Imaging, Vision, Pattern Recognition and Graphics |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Supervised learning -- Probabilistic inference -- Optimal transport -- Optimization -- Quantum, hardware -- Sustainability. |
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Sommario/riassunto |
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The 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 |
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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. |
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