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000733835 020__ $$a9783319235257$$qelectronic book
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000733835 035__ $$aSP(OCoLC)919708614
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000733835 08204 $$a006.3/1$$223
000733835 1112_ $$aECML PKDD (Conference)$$d(2015 :$$cPorto, Portugal)
000733835 24510 $$aMachine learning and knowledge discovery in databases$$h[electronic resource] :$$bEuropean Conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015, Proceedings.$$nPart II /$$cAnnalisa Appice, Pedro Pereira Rodrigues, Vitor Santos Costa, João Gama, Alípio Jorge, Carlos Soares (eds.).
000733835 2463_ $$aECML PKDD 2015
000733835 260__ $$aCham :$$bSpringer,$$c2015.
000733835 300__ $$a1 online resource (xlii, 773 pages) :$$billustrations.
000733835 336__ $$atext$$btxt$$2rdacontent
000733835 337__ $$acomputer$$bc$$2rdamedia
000733835 338__ $$aonline resource$$bcr$$2rdacarrier
000733835 4901_ $$aLecture notes in artificial intelligence,$$x0302-9743 ;$$v9285
000733835 4901_ $$aLNCS sublibrary. SL 7, Artificial intelligence
000733835 500__ $$aInternational conference proceedings.
000733835 500__ $$aIncludes author index.
000733835 506__ $$aAccess limited to authorized users.
000733835 520__ $$aThe 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.
000733835 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed September 3, 2015).
000733835 650_0 $$aMachine learning$$vCongresses.
000733835 650_0 $$aData mining$$vCongresses.
000733835 7001_ $$aAppice, Annalisa,$$eeditor.
000733835 830_0 $$aLecture notes in computer science.$$pLecture notes in artificial intelligence ;$$v9285.
000733835 830_0 $$aLNCS sublibrary.$$nSL 7,$$pArtificial intelligence.
000733835 852__ $$bebk
000733835 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-319-23525-7$$zOnline Access$$91397441.1
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000733835 980__ $$aEBOOK
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