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Algorithmic Learning Theory : 23rd International Conference, ALT 2012, Lyon, France, October 29-31, 2012, Proceedings / edited by Nader H. Bshouty, Gilles Stoltz, Nicolas Vayatis, Thomas Zeugmann.
- Format:
- Book
- Series:
- Computer Science (SpringerNature-11645)
- Lecture notes in computer science. Lecture notes in artificial intelligence 2945-9141 ; 7568
- Lecture Notes in Artificial Intelligence, 2945-9141 ; 7568
- Language:
- English
- Subjects (All):
- Artificial intelligence.
- Machine theory.
- Algorithms.
- Computer science.
- Pattern recognition systems.
- Artificial Intelligence.
- Formal Languages and Automata Theory.
- Theory of Computation.
- Computer Science Logic and Foundations of Programming.
- Automated Pattern Recognition.
- Local Subjects:
- Artificial Intelligence.
- Formal Languages and Automata Theory.
- Algorithms.
- Theory of Computation.
- Computer Science Logic and Foundations of Programming.
- Automated Pattern Recognition.
- Physical Description:
- 1 online resource (XII, 381 pages) : 23 illustrations
- Edition:
- 1st ed. 2012.
- Contained In:
- Springer Nature eBook
- Place of Publication:
- Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2012.
- System Details:
- text file PDF
- Summary:
- This book constitutes the refereed proceedings of the 23rd International Conference on Algorithmic Learning Theory, ALT 2012, held in Lyon, France, in October 2012. The conference was co-located and held in parallel with the 15th International Conference on Discovery Science, DS 2012. The 23 full papers and 5 invited talks presented were carefully reviewed and selected from 47 submissions. The papers are organized in topical sections on inductive inference, teaching and PAC learning, statistical learning theory and classification, relations between models and data, bandit problems, online prediction of individual sequences, and other models of online learning.
- Contents:
- inductive inference
- teaching and PAC learning
- statistical learning theory and classification
- relations between models and data
- bandit problems, online prediction of individual sequences.- other models of online learning.
- Other Format:
- Printed edition:
- ISBN:
- 978-3-642-34106-9
- 9783642341069
- Access Restriction:
- Restricted for use by site license.
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