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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 6331)
Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)
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Conference proceedings info: ALT 2010.
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About this book
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 wasco-located and held in parallel with Algorithmic Learning Theory.
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Keywords
- algorithmic learning theory
- algorithms
- classification
- complexity
- complexity theory
- decision trees
- grammtical inference
- inductive inference
- kolmogorov complexity
- logic programming
- query learning
- statistical learn
- support vector machines
- teaching models
- unsupervised learning
- algorithm analysis and problem complexity
Table of contents (32 papers)
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Front Matter
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Editors’ Introduction
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Regular Contributions
Other volumes
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Algorithmic Learning Theory
Editors and Affiliations
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 Computer Science
DOI: https://doi.org/10.1007/978-3-642-16108-7
Publisher: Springer Berlin, Heidelberg
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer-Verlag Berlin Heidelberg 2010
Softcover ISBN: 978-3-642-16107-0Published: 27 September 2010
eBook ISBN: 978-3-642-16108-7Published: 02 September 2010
Series ISSN: 0302-9743
Series E-ISSN: 1611-3349
Edition Number: 1
Number of Pages: XIII, 421
Number of Illustrations: 45 b/w illustrations
Topics: Artificial Intelligence, Programming Techniques, Mathematical Logic and Formal Languages, Algorithm Analysis and Problem Complexity, Computation by Abstract Devices, Logics and Meanings of Programs