Algorithmic Learning Theory (eBook, PDF)

23rd International Conference, ALT 2012, Lyon, France, October 29-31, 2012, Proceedings
Redaktion: Bshouty, Nader H.; Zeugmann, Thomas; Vayatis, Nicolas; Stoltz, Gilles

• Format: PDF

Produktbeschreibung

This book constitutes the refereed proceedings of the 23rd International Conference on Algorithmic Learning Theory, ALT 2012, held in Lyon, France, in October 2012. The conference was co-located and held in parallel with the 15th International Conference on Discovery Science, DS 2012. The 23 full papers and 5 invited talks presented were carefully reviewed and selected from 47 submissions. The papers are organized in topical sections on inductive inference, teaching and PAC learning, statistical learning theory and classification, relations between models and data, bandit problems, online prediction of individual sequences, and other models of online learning.

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Produktdetails

• Verlag: Springer Berlin Heidelberg
• Seitenzahl: 381
• Erscheinungstermin: 1. Oktober 2012
• Englisch
• ISBN-13: 9783642341069
• Artikelnr.: 44221470

Inhaltsangabe

inductive inference.- teaching and PAC learning.- statistical learning theory and classification.- relations between models and data.- bandit problems, online prediction of individual sequences.- other models of online learning.