Lecture Notes in Artificial Intelligence

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

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 (3 chapters)

  • Optimality Issues of Universal Greedy Agents with Static Priors

    Laurent Orseau

    Pages

  • A PAC-Bayes Bound for Tailored Density Estimation

    Matthew Higgs, John Shawe-Taylor

    Pages

  • Compressed Learning with Regular Concept

    Jiawei Lv, et al.

    Pages

Buy this book

eBook $79.99
price for USA (gross)
  • ISBN 978-3-642-16108-7
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover $109.00
price for USA
  • ISBN 978-3-642-16107-0
  • Free shipping for individuals worldwide
  • Online orders shipping within 2-3 days.
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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 and Tables
45 b/w illustrations
Topics