Algorithmic Learning Theory: 19th International Conference, ALT 2008, Budapest, Hungary, October 13-16, 2008, Proceedings (Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence)

Freund, Yoav [Editor]; Györfi, László [Editor]; Turán, György [Editor]; Zeugmann, Thomas [Editor];

ISBN 10: 3540879862 / ISBN 13: 9783540879862

Verlag: Springer, 2008

Beschreibung

Ex-library paperback in very nice condition with the usual markings and attachments. Bestandsnummer des Verkäufers UTD14a17908

Bibliografische Details

Titel: Algorithmic Learning Theory: 19th ...

Verlag: Springer

Erscheinungsdatum: 2008

Einband: Paperback

Zustand: Very Good

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Inhaltsangabe:

This volume contains papers presented at the 19th International Conference on Algorithmic Learning Theory (ALT 2008), which was held in Budapest, Hungary during October 13-16, 2008. The conference was co-located with the 11th - ternational Conference on Discovery Science (DS 2008). The technical program of ALT 2008 contained 31 papers selected from 46 submissions, and 5 invited talks. The invited talks were presented in joint sessions of both conferences. ALT 2008 was the 19th in the ALT conference series, established in Japan in 1990. The series Analogical and Inductive Inference is a predecessor of this series: it was held in 1986, 1989 and 1992, co-located with ALT in 1994, and s- sequently merged with ALT. ALT maintains its strong connections to Japan, but has also been held in other countries, such as Australia, Germany, Italy, Sin- pore, Spain and the USA. The ALT conference series is supervised by its Steering Committee: Naoki Abe (IBM T. J.

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This book constitutes the refereed proceedings of the 19th International Conference on Algorithmic Learning Theory, ALT 2008, held in Budapest, Hungary, in October 2008, co-located with the 11th International Conference on Discovery Science, DS 2008.

The 31 revised full papers presented together with the abstracts of 5 invited talks were carefully reviewed and selected from 46 submissions. The papers are dedicated to the theoretical foundations of machine learning; they address topics such as statistical learning; probability and stochastic processes; boosting and experts; active and query learning; and inductive inference.