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Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2019, Würzburg, Germany, September 16–20, 2019, Proceedings, Part II - Couverture souple

 
9783030461461: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2019, Würzburg, Germany, September 16–20, 2019, Proceedings, Part II
Couverture souple
ISBN 10 : 3030461467 ISBN 13 : 9783030461461
Editeur : Springer, 2020

Synopsis

The three volume proceedings LNAI 11906 – 11908 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2019, held in Würzburg, Germany, in September 2019.<p>The total of 130 regular papers presented in these volumes was carefully reviewed and selected from 733 submissions; there are 10 papers in the demo track. </p><p>The contributions were organized in topical sections named as follows:</p><p>Part I: pattern mining; clustering, anomaly and outlier detection, and autoencoders; dimensionality reduction and feature selection; social networks and graphs; decision trees, interpretability, and causality; strings and streams; privacy and security; optimization.</p><p>Part II: supervised learning; multi-label learning; large-scale learning; deep learning; probabilistic models; natural language processing.</p><p>Part III: reinforcement learning and bandits; ranking; applied data science: computer vision and explanation; applied data science: healthcare; applied data science: e-commerce, finance, and advertising; applied data science: rich data; applied data science: applications; demo track.</p><p></p><p>Chapter "Incorporating Dependencies in Spectral Kernels for Gaussian Processes" is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.</p><p></p>

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

À propos de la quatrième de couverture

Chapter "Incorporating Dependencies in Spectral Kernels for Gaussian Processes" is&nbsp;available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
Springer
Date d'édition
2020
Langue
anglais
ISBN 10 
3030461467
ISBN 13 
9783030461461
Reliure
Broché
Nombre de pages
732
Éditeur
Brefeld Ulf, Fromont Elisa, Hotho Andreas, Knobbe Arno, Maathuis Marloes
Coordonnées du fabricant
Springer Nature Customer Service Center GmbH
ProductSafety@springernature.com

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