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001 978-3-642-33486-3
003 DE-He213
005 20140220083327.0
007 cr nn 008mamaa
008 120910s2012 gw | s |||| 0|eng d
020 _a9783642334863
_9978-3-642-33486-3
024 7 _a10.1007/978-3-642-33486-3
_2doi
050 4 _aQA76.9.D343
072 7 _aUNF
_2bicssc
072 7 _aUYQE
_2bicssc
072 7 _aCOM021030
_2bisacsh
082 0 4 _a006.312
_223
100 1 _aFlach, Peter A.
_eeditor.
245 1 0 _aMachine Learning and Knowledge Discovery in Databases
_h[electronic resource] :
_bEuropean Conference, ECML PKDD 2012, Bristol, UK, September 24-28, 2012. Proceedings, Part II /
_cedited by Peter A. Flach, Tijl Bie, Nello Cristianini.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2012.
300 _aXXVI, 867 p. 245 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aLecture Notes in Computer Science,
_x0302-9743 ;
_v7524
505 0 _aPrivacy and security -- rankings and recommendations -- reinforcement learning and planning -- rule mining and subgroup discovery -- semi-supervised and transductive learning -- sensor data -- sequence and string mining -- social network mining -- spatial and geographical data mining -- statistical methods and evaluation -- time series and temporal data mining -- transfer learning.
520 _aThis two-volume set LNAI 7523 and LNAI 7524 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2012, held in Bristol, UK, in September 2012. The 105 revised research papers presented together with 5 invited talks were carefully reviewed and selected from 443 submissions. The final sections of the proceedings are devoted to Demo and Nectar papers. The Demo track includes 10 papers (from 19 submissions) and the Nectar track includes 4 papers (from 14 submissions). The papers grouped in topical sections on association rules and frequent patterns; Bayesian learning and graphical models; classification; dimensionality reduction, feature selection and extraction; distance-based methods and kernels; ensemble methods; graph and tree mining; large-scale, distributed and parallel mining and learning; multi-relational mining and learning; multi-task learning; natural language processing; online learning and data streams; privacy and security; rankings and recommendations; reinforcement learning and planning; rule mining and subgroup discovery; semi-supervised and transductive learning; sensor data; sequence and string mining; social network mining; spatial and geographical data mining; statistical methods and evaluation; time series and temporal data mining; and transfer learning.
650 0 _aComputer science.
650 0 _aComputational complexity.
650 0 _aData mining.
650 0 _aInformation storage and retrieval systems.
650 0 _aArtificial intelligence.
650 0 _aOptical pattern recognition.
650 1 4 _aComputer Science.
650 2 4 _aData Mining and Knowledge Discovery.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aPattern Recognition.
650 2 4 _aDiscrete Mathematics in Computer Science.
650 2 4 _aProbability and Statistics in Computer Science.
650 2 4 _aInformation Storage and Retrieval.
700 1 _aBie, Tijl.
_eeditor.
700 1 _aCristianini, Nello.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783642334856
830 0 _aLecture Notes in Computer Science,
_x0302-9743 ;
_v7524
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-642-33486-3
912 _aZDB-2-SCS
912 _aZDB-2-LNC
999 _c103647
_d103647