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Machine Learning and Knowledge Discovery in Databases : European Conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015, Proceedings, Part III (Lecture Notes in Computer Science)

By: Albert Bifet (Edited by) , Bianca Zadrozny (Edited by) , Dino Pedreschi (Edited by) , Francesco Bonchi (Edited by) , Jaime Cardoso (Edited by) , Michael May (Edited by) , Myra Spiliopoulou (Edited by) , Ricard Gavalda (Edited by)

Extended Catalogue

Ksh 9,950.00

Format: Paperback or Softback

ISBN-10: 3319234609

ISBN-13: 9783319234601

Collection / Series: Lecture Notes in Computer Science

Collection Type: Publisher collection

Edition statement: 1st ed. 2015

Publisher: Springer International Publishing AG

Imprint: Springer International Publishing AG

Country of Manufacture: CH

Country of Publication: GB

Publication Date: Sep 11th, 2015

Publication Status: Active

Product extent: 345 Pages

Product Classification / Subject(s): Data mining
Machine learning

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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. These include 89 research papers, 11 industrial papers, 14 nectar papers, 17 demo papers.
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.

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