Algorithmic Learning Theory
21st International Conference, ALT 2010, Canberra, Australia, October 6-8, 2010. Proceedings
Editors: Hutter, M., Stephan, F., Vovk, V., Zeugmann, Th. (Eds.)
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- About this book
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This volume contains the papers presented at the 21st International Conf- ence on Algorithmic Learning Theory (ALT 2010), which was held in Canberra, Australia, October 6–8, 2010. The conference was co-located with the 13th - ternational Conference on Discovery Science (DS 2010) and with the Machine Learning Summer School, which was held just before ALT 2010. The tech- cal program of ALT 2010, contained 26 papers selected from 44 submissions and ?ve invited talks. The invited talks were presented in joint sessions of both conferences. ALT 2010 was dedicated to the theoretical foundations of machine learning and took place on the campus of the Australian National University, Canberra, Australia. ALT provides a forum for high-quality talks with a strong theore- cal background and scienti?c interchange in areas such as inductive inference, universal prediction, teaching models, grammatical inference, formal languages, inductive logic programming, query learning, complexity of learning, on-line learning and relative loss bounds, semi-supervised and unsupervised learning, clustering,activelearning,statisticallearning,supportvectormachines,Vapnik- Chervonenkisdimension,probablyapproximatelycorrectlearning,Bayesianand causal networks, boosting and bagging, information-based methods, minimum descriptionlength,Kolmogorovcomplexity,kernels,graphlearning,decisiontree methods, Markov decision processes, reinforcement learning, and real-world - plications of algorithmic learning theory. DS 2010 was the 13th International Conference on Discovery Science and focused on the development and analysis of methods for intelligent data an- ysis, knowledge discovery and machine learning, as well as their application to scienti?c knowledge discovery. As is the tradition, it was co-located and held in parallel with Algorithmic Learning Theory.
- Table of contents (32 chapters)
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Editors’ Introduction
Pages 1-10
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Towards General Algorithms for Grammatical Inference
Pages 11-30
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The Blessing and the Curse of the Multiplicative Updates
Pages 31-31
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Discovery of Abstract Concepts by a Robot
Pages 32-32
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Contrast Pattern Mining and Its Application for Building Robust Classifiers
Pages 33-33
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Table of contents (32 chapters)
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Bibliographic Information
- Bibliographic Information
-
- Book Title
- Algorithmic Learning Theory
- Book Subtitle
- 21st International Conference, ALT 2010, Canberra, Australia, October 6-8, 2010. Proceedings
- Editors
-
- Marcus Hutter
- Frank Stephan
- Vladimir Vovk
- Thomas Zeugmann
- Series Title
- Lecture Notes in Artificial Intelligence
- Series Volume
- 6331
- Copyright
- 2010
- Publisher
- Springer-Verlag Berlin Heidelberg
- Copyright Holder
- Springer-Verlag Berlin Heidelberg
- eBook ISBN
- 978-3-642-16108-7
- DOI
- 10.1007/978-3-642-16108-7
- Softcover ISBN
- 978-3-642-16107-0
- Edition Number
- 1
- Number of Pages
- XIII, 421
- Number of Illustrations
- 45 b/w illustrations
- Topics
