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|a 9783642237805
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| 100 |
1 |
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|a Gunopulos, Dimitrios
|e [editor]
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| 245 |
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|a Machine Learning and Knowledge Discovery in Databases
|h Elektronische Ressource
|b European Conference, ECML PKDD 2010, Athens, Greece, September 5-9, 2011, Proceedings, Part I
|c edited by Dimitrios Gunopulos, Thomas Hofmann, Donato Malerba, Michalis Vazirgiannis
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| 250 |
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|a 1st ed. 2011
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| 260 |
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|a Berlin, Heidelberg
|b Springer Berlin Heidelberg
|c 2011, 2011
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| 300 |
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|a XXX, 649 p
|b online resource
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| 653 |
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|a Computer science—Mathematics
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| 653 |
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|a Mathematical statistics
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| 653 |
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|a Probability and Statistics in Computer Science
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| 653 |
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|a Algorithms
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| 653 |
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|a Artificial Intelligence
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| 653 |
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|a Information Storage and Retrieval
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| 653 |
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|a Formal Languages and Automata Theory
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| 653 |
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|a Database Management
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| 653 |
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|a Information storage and retrieval systems
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| 653 |
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|a Machine theory
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| 653 |
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|a Artificial intelligence
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| 653 |
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|a Database management
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| 700 |
1 |
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|a Hofmann, Thomas
|e [editor]
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| 700 |
1 |
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|a Malerba, Donato
|e [editor]
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| 700 |
1 |
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|a Vazirgiannis, Michalis
|e [editor]
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b Springer
|a Springer eBooks 2005-
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| 490 |
0 |
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|a Lecture Notes in Artificial Intelligence
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| 028 |
5 |
0 |
|a 10.1007/978-3-642-23780-5
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| 856 |
4 |
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|u https://doi.org/10.1007/978-3-642-23780-5?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
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|a 006.3
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| 520 |
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|a This three-volume set LNAI 6911, LNAI 6912, and LNAI 6913 constitutes the refereed proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2011, held in Athens, Greece, in September 2011. The 121 revised full papers presented together with 10 invited talks and 11 demos in the three volumes, were carefully reviewed and selected from about 600 paper submissions. The papers address all areas related to machine learning and knowledge discovery in databases as well as other innovative application domains such as supervised and unsupervised learning with some innovative contributions in fundamental issues; dimensionality reduction, distance and similarity learning, model learning and matrix/tensor analysis; graph mining, graphical models, hidden markov models, kernel methods, active and ensemble learning, semi-supervised and transductive learning, mining sparse representations, model learning, inductive logic programming, and statistical learning. a significant part of the papers covers novel and timely applications of data mining and machine learning in industrial domains
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