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Machine Learning and Knowledge Discovery in Databases, Part II : European Conference, ECML PKDD 2010, Athens, Greece, September 5-9, 2011, Proceedings, Part II / edited by Dimitrios Gunopulos, Thomas Hofmann, Donato Malerba, Michalis Vazirgiannis.
- Format:
- Book
- Series:
- Computer Science (SpringerNature-11645)
- Lecture notes in computer science. Lecture notes in artificial intelligence 2945-9141 ; 6912
- Lecture Notes in Artificial Intelligence, 2945-9141 ; 6912
- Language:
- English
- Subjects (All):
- Artificial intelligence.
- Database management.
- Information storage and retrieval systems.
- Machine theory.
- Algorithms.
- Computer science-Mathematics.
- Mathematical statistics.
- Artificial Intelligence.
- Database Management.
- Information Storage and Retrieval.
- Formal Languages and Automata Theory.
- Probability and Statistics in Computer Science.
- Local Subjects:
- Artificial Intelligence.
- Database Management.
- Information Storage and Retrieval.
- Formal Languages and Automata Theory.
- Algorithms.
- Probability and Statistics in Computer Science.
- Physical Description:
- 1 online resource (XXII, 681 pages) : 163 illustrations, 113 illustrations in color.
- Edition:
- 1st ed. 2011.
- Contained In:
- Springer Nature eBook
- Place of Publication:
- Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2011.
- System Details:
- text file PDF
- Summary:
- 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.
- Other Format:
- Printed edition:
- ISBN:
- 978-3-642-23783-6
- 9783642237836
- Access Restriction:
- Restricted for use by site license.
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