Algorithmic Learning Theory

. Ed(s): Hutter, Marcus; Stephan, Frank; Vovk, Vladimir; Zeugmann, Thomas

ISBN 10: 3642161073 / ISBN 13: 9783642161070

Published by Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, 2010

About this Item

Constitutes the refereed proceedings of the 21th International Conference on Algorithmic Learning Theory, ALT 2010, that was held in Canberra, Australia. Editor(s): Hutter, Marcus; Stephan, Frank; Vovk, Vladimir; Zeugmann, Thomas. Series: Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence. Num Pages: 421 pages, 45 black & white illustrations, biography. BIC Classification: JNV; UMB; UMX; UYQM. Category: (P) Professional & Vocational. Dimension: 231 x 155 x 25. Weight in Grams: 640. . 2010. Paperback. . . . . Books ship from the US and Ireland.

Seller Inventory # V9783642161070

Bibliographic Details

• Title: Algorithmic Learning Theory
• Publisher: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
• Publication Date: 2010
• Language: English
• ISBN 10: 3642161073
• ISBN 13: 9783642161070
• Binding: Soft cover
• Condition: New

About this title

Synopsis

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.