Machine learning and knowledge discovery in databases : European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings. Part II /

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Bibliographic Details
Meeting name:	ECML PKDD (Conference) (2011 : Athens, Greece)
Imprint:	Heidelberg ; New York : Springer, ©2011.
Description:	1 online resource (xxii, 681 pages) : illustrations.
Language:	English
Series:	Lecture notes in artificial intelligence ; 6912 Lecture notes in computer science LNCS sublibrary. SL 7, Artificial intelligence Lecture notes in computer science. Lecture notes in artificial intelligence ; 6912. Lecture notes in computer science. LNCS sublibrary. SL 7, Artificial intelligence.
Subject:	Machine learning -- Congresses. Data mining -- Congresses. Databases -- Congresses. Data mining. Databases. Machine learning. Conference papers and proceedings.
Format:	E-Resource Book
URL for this record:	http://pi.lib.uchicago.edu/1001/cat/bib/11076008

Hidden Bibliographic Details
Other authors / contributors:	Gunopulos, Dimitrios, 1967-
ISBN:	9783642237836 3642237835 9783642237829
Notes:	Includes bibliographical references and author index.
Summary:	This three-volume set LNAI 6911, LNAI 6912, and LNAI 6913 constitutes the refereed proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2011, held in Athens, Greece, in September 2011. The 121 revised full papers presented together with 10 invited talks and 11 demos in the three volumes, were carefully reviewed and selected from about 600 paper submissions. The papers address all areas related to machine learning and knowledge discovery in databases as well as other innovative application domains such as supervised and unsupervised learning with some innovative contributions in fundamental issues; dimensionality reduction, distance and similarity learning, model learning and matrix/tensor analysis; graph mining, graphical models, hidden markov models, kernel methods, active and ensemble learning, semi-supervised and transductive learning, mining sparse representations, model learning, inductive logic programming, and statistical learning. a significant part of the papers covers novel and timely applications of data mining and machine learning in industrial domains.

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