Machine Learning and Knowledge Discovery in Databases : European Conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015, Proceedings, Part I / edited by Annalisa Appice, Pedro Pereira Rodrigues, Vítor Santos Costa, Carlos Soares, João Gama, Alípio Jorge.

Format:

Book

Contributor:

Appice, Annalisa, editor.

Rodrigues, Pedro Pereira, editor.

Santos Costa, Vítor, 1961- editor.

Soares, Carlos, editor.

Gama, João, editor.

Jorge, Alípio, editor.

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Lecture notes in computer science. Lecture notes in artificial intelligence ; 9284.

Lecture Notes in Artificial Intelligence ; 9284

Language:

English

Subjects (All):

Data mining.

Artificial intelligence.

Pattern perception.

Information storage and retrieval.

Database management.

Application software.

Data Mining and Knowledge Discovery.

Artificial Intelligence.

Pattern Recognition.

Information Storage and Retrieval.

Database Management.

Information Systems Applications (incl. Internet).

Local Subjects:

Data Mining and Knowledge Discovery.

Artificial Intelligence.

Pattern Recognition.

Information Storage and Retrieval.

Database Management.

Information Systems Applications (incl. Internet).

Physical Description:

1 online resource (LVIII, 709 pages) : 160 illustrations.

Edition:

First edition 2015.

Contained In:

Springer eBooks

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2015.

System Details:

text file PDF

Summary:

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, and 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.

Other Format:

Printed edition:

ISBN:

978-3-319-23528-8

9783319235288

Access Restriction:

Restricted for use by site license.