Band 9285
Machine Learning and Knowledge Discovery in Databases European Conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015, Proceedings, Part II
Aus der Reihe
Lecture Notes in Computer Science
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- Taschenbuch ausgewählt
- eBook
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Sprache:Englisch
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Auflage:1st ed. 2015
- 1st ed. 2015 Fr. 72.90 ausgewählt
- 1st ed. 2015 Fr. 72.90
Fr. 72.90
inkl. gesetzl. MwSt.,
Beschreibung
Produktdetails
Einband
Taschenbuch
Erscheinungsdatum
14.09.2015
Abbildungen
XLII, 773 p. 198 illus.
Herausgeber
Annalisa Appice + weitereVerlag
SpringerSeitenzahl
773
Maße (L/B/H)
23.5/15.5/4.4 cm
Gewicht
1212 g
Auflage
1st ed. 2015
Sprache
Englisch
ISBN
978-3-319-23524-0
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.
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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