Machine Learning and Knowledge Discovery in Databases
European Conference, ECML PKDD 2014, Nancy, France, September 15-19, 2014. Proceedings, Part III
Published on 23. September 2014
Book
Paperback/Softback
XXXVI, 533 pages
978-3-662-44844-1 (ISBN)
Description
This three-volume set LNAI 8724, 8725 and 8726 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2014, held in Nancy, France, in September 2014. The 115 revised research papers presented together with 13 demo track papers, 10 nectar track papers, 8 PhD track papers, and 9 invited talks were carefully reviewed and selected from 550 submissions. The papers cover the latest high-quality interdisciplinary research results in all areas related to machine learning and knowledge discovery in databases.
More details
Series
Edition
2014 ed.
Language
English
Place of publication
Berlin
Germany
Publishing group
Springer Berlin
Target group
Professional and scholarly
Research
Illustrations
155 s/w Abbildungen
XXXVI, 533 p. 155 illus.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 31 mm
Weight
855 gr
ISBN-13
978-3-662-44844-1 (9783662448441)
DOI
10.1007/978-3-662-44845-8
Schweitzer Classification
Other editions
Additional editions
Toon Calders | Floriana Esposito | Eyke Hüllermeier
Machine Learning and Knowledge Discovery in Databases
European Conference, ECML PKDD 2014, Nancy, France, September 15-19, 2014. Proceedings, Part III
E-Book
09/2014
Springer
€53.49
Available for download
Content
Dynamic networks and knowledge discovery.- Interactions between data mining and natural language processing.- Mining ubiquitous and social environments.- Statistically sound data mining.- Machine learning for urban sensor data.- Multi-target prediction.- Representation learning.- Neural connectomics: from imaging to connectivity.- Data analytics for renewable energy integration.- Linked data for knowledge discovery.- New frontiers in mining complex patterns.- Experimental economics and machine learning.- Learning with multiple views: applications to computer vision and multimedia mining.- Generalization and reuse of machine learning models over multiple contexts.- Predictive web analytics.