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Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2022, Grenoble, France, September 19–23, 2022, Proceedings, Part VI: Lecture Notes in Computer Science, cartea 13718

Editat de Massih-Reza Amini, Stéphane Canu, Asja Fischer, Tias Guns, Petra Kralj Novak, Grigorios Tsoumakas
en Limba Engleză Paperback – 18 mar 2023
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 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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Specificații

ISBN-13: 9783031264214
ISBN-10: 3031264215
Pagini: 669
Ilustrații: XLVIII, 669 p. 205 illus., 191 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.99 kg
Ediția:1st ed. 2023
Editura: Springer Nature Switzerland
Colecția Springer
Seriile Lecture Notes in Computer Science, Lecture Notes in Artificial Intelligence

Locul publicării:Cham, Switzerland

Cuprins

Time series.- Financial machine learning.- Applications.- Applications: transportation.- Demo track.

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