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Record Nr. |
UNISA996466030603316 |
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Titolo |
Machine Learning and Knowledge Discovery in Databases [[electronic resource] ] : European Conference, ECML PKDD 2010, Athens, Greece, September 5-9, 2011, Proceedings, Part I / / edited by Dimitrios Gunopulos, Thomas Hofmann, Donato Malerba, Michalis Vazirgiannis |
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Pubbl/distr/stampa |
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Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2011 |
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ISBN |
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Edizione |
[1st ed. 2011.] |
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Descrizione fisica |
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1 online resource (XXX, 649 p.) |
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Collana |
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Lecture Notes in Artificial Intelligence ; ; 6911 |
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Disciplina |
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Soggetti |
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Artificial intelligence |
Database management |
Information storage and retrieval |
Mathematical logic |
Algorithms |
Mathematical statistics |
Artificial Intelligence |
Database Management |
Information Storage and Retrieval |
Mathematical Logic and Formal Languages |
Algorithm Analysis and Problem Complexity |
Probability and Statistics in Computer Science |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Bibliographic Level Mode of Issuance: Monograph |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Sommario/riassunto |
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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 |
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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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