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000733857 019__ $$a919708681
000733857 020__ $$a9783319235288$$qelectronic book
000733857 020__ $$a3319235281$$qelectronic book
000733857 020__ $$z9783319235271
000733857 0247_ $$a10.1007/978-3-319-23528-8$$2doi
000733857 035__ $$aSP(OCoLC)ocn919907623
000733857 035__ $$aSP(OCoLC)919907623$$z(OCoLC)919708681
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000733857 08204 $$a006.3/1$$223
000733857 1112_ $$aECML PKDD (Conference)$$d(2015 :$$cPorto, Portugal)
000733857 24510 $$aMachine learning and knowledge discovery in databases$$h[electronic resource] :$$bEuropean Conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015, Proceedings.$$nPart I /$$cAnnalisa Appice, Pedro Pereira Rodrigues, Vitor Santos Costa, Carlos Soares, João Gama, Alípio Jorge (eds.).
000733857 2463_ $$aECML PKDD 2015
000733857 264_1 $$aCham :$$bSpringer,$$c2015.
000733857 300__ $$a1 online resource (lviii, 709 pages) :$$billustrations.
000733857 336__ $$atext$$btxt$$2rdacontent
000733857 337__ $$acomputer$$bc$$2rdamedia
000733857 338__ $$aonline resource$$bcr$$2rdacarrier
000733857 4901_ $$aLecture notes in artificial intelligence,$$x0302-9743 ;$$v9284
000733857 4901_ $$aLNCS sublibrary. SL 7, Artificial intelligence
000733857 500__ $$aInternational conference proceedings.
000733857 500__ $$aIncludes author index.
000733857 506__ $$aAccess limited to authorized users.
000733857 520__ $$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, 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.
000733857 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed September 4, 2015).
000733857 650_0 $$aMachine learning$$vCongresses.
000733857 650_0 $$aData mining$$vCongresses.
000733857 7001_ $$aAppice, Annalisa,$$eeditor.
000733857 7001_ $$aRodrigues, Pedro Pereira,$$eeditor.
000733857 7001_ $$aSantos Costa, Vítor,$$d1961-$$eeditor.
000733857 7001_ $$aSoares, Carlos,$$eeditor.
000733857 7001_ $$aGama, João,$$eeditor.
000733857 7001_ $$aJorge, Alípio,$$eeditor.
000733857 830_0 $$aLecture notes in computer science.$$pLecture notes in artificial intelligence ;$$v9284.
000733857 830_0 $$aLNCS sublibrary.$$nSL 7,$$pArtificial intelligence.
000733857 852__ $$bebk
000733857 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-319-23528-8$$zOnline Access$$91397441.1
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000733857 980__ $$aBIB
000733857 982__ $$aEbook
000733857 983__ $$aOnline
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