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024 7 _a10.1007/978-3-642-33460-3
_2doi
050 4 _aQA76.9.D343
072 7 _aUNF
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072 7 _aUYQE
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082 0 4 _a006.312
_223
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 I /
_cedited by Peter A. Flach, Tijl De Bie, Nello Cristianini.
250 _a1st ed. 2012.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2012.
300 _aXXVI, 879 p. 241 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 Artificial Intelligence,
_x2945-9141 ;
_v7523
505 0 _aAassociation 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.
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 _aData mining.
650 0 _aArtificial intelligence.
650 0 _aPattern recognition systems.
650 0 _aComputer science
_xMathematics.
650 0 _aDiscrete mathematics.
650 0 _aMathematical statistics.
650 0 _aInformation storage and retrieval systems.
650 1 4 _aData Mining and Knowledge Discovery.
650 2 4 _aArtificial Intelligence.
650 2 4 _aAutomated Pattern 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 _aFlach, Peter A.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aDe Bie, Tijl.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aCristianini, Nello.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783642334597
776 0 8 _iPrinted edition:
_z9783642334610
830 0 _aLecture Notes in Artificial Intelligence,
_x2945-9141 ;
_v7523
856 4 0 _uhttps://doi.org/10.1007/978-3-642-33460-3
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912 _aZDB-2-SXCS
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