Machine Learning and Knowledge Discovery in Databases

Albert Bifet

ISBN 10: 3319234609 / ISBN 13: 9783319234601

Published by Springer International Publishing Sep 2015, 2015

About this Item

This item is printed on demand - it takes 3-4 days longer - Neuware -The three volume set LNAI 9284, 9285, and 9286 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2015, held in Porto, Portugal, in September 2015.The 131 papers presented in these proceedings were carefully reviewed and selected from a total of 483 submissions. These include 89 research papers, 11 industrial papers, 14 nectar papers, 17 demo papers. They were organized in topical sections named: classification, regression and supervised learning; clustering and unsupervised learning; data preprocessing; data streams and online learning; deep learning; distance and metric learning; large scale learning and big data; matrix and tensor analysis; pattern and sequence mining; preference learning and label ranking; probabilistic, statistical, and graphical approaches; rich data; and social and graphs. Part III is structured in industrial track, nectar track, and demo track. 376 pp. Englisch.

Seller Inventory # 9783319234601

Bibliographic Details

• Title: Machine Learning and Knowledge Discovery in Databases
• Publisher: Springer International Publishing Sep 2015
• Publication Date: 2015
• Language: English
• ISBN 10: 3319234609
• ISBN 13: 9783319234601
• Binding: Taschenbuch
• Condition: Neu

About this title

Synopsis

The three volume set LNAI 9284, 9285, and 9286 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2015, held in Porto, Portugal, in September 2015. The 131 papers presented in these proceedings were carefully reviewed and selected from a total of 483 submissions. These include 89 research papers, 11 industrial papers, 14 nectar papers, 17 demo papers. They were organized in topical sections named: classification, regression and supervised learning; clustering and unsupervised learning; data preprocessing; data streams and online learning; deep learning; distance and metric learning; large scale learning and big data; matrix and tensor analysis; pattern and sequence mining; preference learning and label ranking; probabilistic, statistical, and graphical approaches; rich data; and social and graphs. Part III is structured in industrial track, nectar track, and demo track.