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|a 9783642409882
|9 978-3-642-40988-2
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|a 10.1007/978-3-642-40988-2
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|a 006.312
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|a Machine Learning and Knowledge Discovery in Databases
|h [electronic resource] :
|b European Conference, ECML PKDD 2013, Prague, Czech Republic, September 23-27, 2013, Proceedings, Part I /
|c edited by Hendrik Blockeel, Kristian Kersting, Siegfried Nijssen, Filip Železný.
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|a 1st ed. 2013.
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|a Berlin, Heidelberg :
|b Springer Berlin Heidelberg :
|b Imprint: Springer,
|c 2013.
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|a LIV, 691 p. 198 illus.
|b online resource.
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|a text
|b txt
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|a Lecture Notes in Artificial Intelligence,
|x 2945-9141 ;
|v 8188
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|a Reinforcement learning -- Markov decision processes -- Active learning and optimization -- Learning from sequences -- Time series and spatio-temporal data -- Data streams -- Graphs and networks -- Social network analysis -- Natural language processing and information extraction -- Ranking and recommender systems -- Matrix and tensor analysis -- Structured output prediction, multi-label and multi-task learning -- Transfer learning -- Bayesian learning -- Graphical models -- Nearest-neighbor methods -- Ensembles -- Statistical learning -- Semi-supervised learning -- Unsupervised learning -- Subgroup discovery, outlier detection and anomaly detection -- Privacy and security -- Evaluation -- Applications -- Medical applications.
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|a This three-volume set LNAI 8188, 8189 and 8190 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2013, held in Prague, Czech Republic, in September 2013. The 111 revised research papers presented together with 5 invited talks were carefully reviewed and selected from 447 submissions. The papers are organized in topical sections on reinforcement learning; Markov decision processes; active learning and optimization; learning from sequences; time series and spatio-temporal data; data streams; graphs and networks; social network analysis; natural language processing and information extraction; ranking and recommender systems; matrix and tensor analysis; structured output prediction, multi-label and multi-task learning; transfer learning; bayesian learning; graphical models; nearest-neighbor methods; ensembles; statistical learning; semi-supervised learning; unsupervised learning; subgroup discovery, outlier detection and anomaly detection; privacy and security; evaluation; applications; medical applications; nectar track; demo track.
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|a Data mining.
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|a Artificial intelligence.
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|a Pattern recognition systems.
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|a Computer science-Mathematics.
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|a Discrete mathematics.
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|a Mathematical statistics.
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|a Information storage and retrieval systems.
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|a Data Mining and Knowledge Discovery.
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|a Artificial Intelligence.
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|a Automated Pattern Recognition.
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2 |
4 |
|a Discrete Mathematics in Computer Science.
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650 |
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4 |
|a Probability and Statistics in Computer Science.
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650 |
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4 |
|a Information Storage and Retrieval.
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1 |
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|a Blockeel, Hendrik.
|e editor.
|4 edt
|4 http://id.loc.gov/vocabulary/relators/edt
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700 |
1 |
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|a Kersting, Kristian.
|e editor.
|4 edt
|4 http://id.loc.gov/vocabulary/relators/edt
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700 |
1 |
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|a Nijssen, Siegfried.
|e editor.
|4 edt
|4 http://id.loc.gov/vocabulary/relators/edt
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700 |
1 |
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|a Železný, Filip.
|e editor.
|4 edt
|4 http://id.loc.gov/vocabulary/relators/edt
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710 |
2 |
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|a SpringerLink (Online service)
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|t Springer Nature eBook
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776 |
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|i Printed edition:
|z 9783642409899
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776 |
0 |
8 |
|i Printed edition:
|z 9783642409875
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830 |
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|a Lecture Notes in Artificial Intelligence,
|x 2945-9141 ;
|v 8188
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856 |
4 |
0 |
|u https://doi.uam.elogim.com/10.1007/978-3-642-40988-2
|z Texto Completo
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|a ZDB-2-SCS
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|a ZDB-2-SXCS
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|a ZDB-2-LNC
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|a Computer Science (SpringerNature-11645)
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950 |
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|a Computer Science (R0) (SpringerNature-43710)
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