Machine Learning and Knowledge Discovery in Databases

Name: Machine Learning and Knowledge Discovery in Databases
Brand: Springer, Berlin
SKU: 9783031264115
Price: 89.64 EUR
Availability: InStock

European Conference, ECML PKDD 2022, Grenoble, France, September 19-23, 2022, Proceedings, Part IV

Автор: Massih-Reza Amini, Stéphane Canu, Asja Fischer, Tias Guns, Petra Krali Novak, Grigorios Tsoumakas

Език:

Корици: С меки корици

Издател: Springer, Berlin

Наличност: Външен склад

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89.64 € 175.32 лв

The multi-volume set LNAI 13713 until 13718 constitutes the refereed proceedings of the European Con...

Информация за книгата

Автор

Massih-Reza Amini, Stéphane Canu, Asja Fischer, Tias Guns, Petra Krali Novak, Grigorios Tsoumakas

Език

Английски език

Корици

Книга - С меки корици

Издадена

2023

страници

688

EAN

9783031264115

Enbook ID

42806305

Издател

Springer, Berlin

Теглоt

947

Размери

155 x 235

Пълно описание

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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