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020 _a9783030657420
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024 7 _a10.1007/978-3-030-65742-0
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
245 1 0 _aAdvanced Analytics and Learning on Temporal Data
_h[electronic resource] :
_b5th ECML PKDD Workshop, AALTD 2020, Ghent, Belgium, September 18, 2020, Revised Selected Papers /
_cedited by Vincent Lemaire, Simon Malinowski, Anthony Bagnall, Thomas Guyet, Romain Tavenard, Georgiana Ifrim.
250 _a1st ed. 2020.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2020.
300 _aX, 233 p. 88 illus., 67 illus. in color.
_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 ;
_v12588
505 0 _aTemporal Data Clustering -- Classification of Univariate and Multivariate Time Series -- Early Classification of Temporal Data -- Deep Learning and Learning Representations for Temporal Data -- Modeling Temporal Dependencies -- Advanced Forecasting and Prediction Models -- Space-Temporal Statistical Analysis -- Functional Data Analysis Methods -- Temporal Data Streams -- Interpretable Time-Series Analysis Methods -- Dimensionality Reduction, Sparsity, Algorithmic Complexity and Big Data Challenge -- Bio-Informatics, Medical, Energy Consumption, Temporal Data.
520 _aThis book constitutes the refereed proceedings of the 4th ECML PKDD Workshop on Advanced Analytics and Learning on Temporal Data, AALTD 2019, held in Ghent, Belgium, in September 2020. The 15 full papers presented in this book were carefully reviewed and selected from 29 submissions. The selected papers are devoted to topics such as Temporal Data Clustering; Classification of Univariate and Multivariate Time Series; Early Classification of Temporal Data; Deep Learning and Learning Representations for Temporal Data; Modeling Temporal Dependencies; Advanced Forecasting and Prediction Models; Space-Temporal Statistical Analysis; Functional Data Analysis Methods; Temporal Data Streams; Interpretable Time-Series Analysis Methods; Dimensionality Reduction, Sparsity, Algorithmic Complexity and Big Data Challenge; and Bio-Informatics, Medical, Energy Consumption, Temporal Data.
650 0 _aArtificial intelligence.
650 0 _aData mining.
650 0 _aSocial sciences
_xData processing.
650 0 _aMachine learning.
650 0 _aEducation
_xData processing.
650 0 _aComputer networks .
650 1 4 _aArtificial Intelligence.
650 2 4 _aData Mining and Knowledge Discovery.
650 2 4 _aComputer Application in Social and Behavioral Sciences.
650 2 4 _aMachine Learning.
650 2 4 _aComputers and Education.
650 2 4 _aComputer Communication Networks.
700 1 _aLemaire, Vincent.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aMalinowski, Simon.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aBagnall, Anthony.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aGuyet, Thomas.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aTavenard, Romain.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aIfrim, Georgiana.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030657413
776 0 8 _iPrinted edition:
_z9783030657437
830 0 _aLecture Notes in Artificial Intelligence,
_x2945-9141 ;
_v12588
856 4 0 _uhttps://doi.org/10.1007/978-3-030-65742-0
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