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04210nam a22006135i 4500 |
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978-3-642-40988-2 |
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|a 9783642409882
|9 978-3-642-40988-2
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|a 10.1007/978-3-642-40988-2
|2 doi
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|a QA76.9.D343
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|a UNF
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|a COM021030
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|a 006.312
|2 23
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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 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 computer
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|a online resource
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|a text file
|b PDF
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|a Lecture Notes in Computer Science,
|x 0302-9743 ;
|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 Computer science.
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|a Computer science
|x Mathematics.
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|a Mathematical statistics.
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|a Data mining.
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|a Information storage and retrieval.
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|a Artificial intelligence.
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|a Pattern recognition.
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|a Computer Science.
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|a Data Mining and Knowledge Discovery.
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|a Artificial Intelligence (incl. Robotics).
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|a Pattern Recognition.
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|a Discrete Mathematics in Computer Science.
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|a Probability and Statistics in Computer Science.
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|a Information Storage and Retrieval.
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|a Blockeel, Hendrik.
|e editor.
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|a Kersting, Kristian.
|e editor.
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|a Nijssen, Siegfried.
|e editor.
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|a Železný, Filip.
|e editor.
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|a SpringerLink (Online service)
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|t Springer eBooks
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|i Printed edition:
|z 9783642409875
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|a Lecture Notes in Computer Science,
|x 0302-9743 ;
|v 8188
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|u http://dx.doi.org/10.1007/978-3-642-40988-2
|z Full Text via HEAL-Link
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|a ZDB-2-SCS
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912 |
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|a ZDB-2-LNC
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|a Computer Science (Springer-11645)
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