Machine Learning and Knowledge Discovery in Databases: European Conference, Ecml Pkdd 2022, Grenoble, France, September 19-23, 2022, Proceedings

Amini, Massih-Reza (Editor)/ Canu, Stéphane (Editor)/ Fischer, Asja (Editor)/ Guns, Tias (Editor)/ Novak, Petra Krali (Editor)

ISBN 10: 3031263898 / ISBN 13: 9783031263897

Editore: Springer-Nature New York Inc, 2023

806 pages. 9.25x6.10x1.60 inches. In Stock. Codice articolo 3031263898

Riassunto:

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.

Dalla quarta di copertina:

Chapters “On the Current State of Reproducibility and Reporting of Uncertainty for Aspect-Based Sentiment

Analysis” and “Contextualized Graph Embeddings for Adverse Drug Event Detection” are licensed under the

terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/

by/4.0/). For further details see license information in the chapter.

Dati bibliografici

Titolo: Machine Learning and Knowledge Discovery in ...
Casa editrice: Springer-Nature New York Inc
Data di pubblicazione: 2023
Legatura: Paperback
Condizione: Brand New