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Tutorial: Learning Topics in Game-Theoretic Decision Making Read chapter Read first chapter

2003 | OriginalPaper | Chapter

PAC-MDL Bounds

Authors : Avrim Blum, John Langford

Published in: Learning Theory and Kernel Machines

Publisher: Springer Berlin Heidelberg

Included in: Professional Book Archive

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We point out that a number of standard sample complexity bounds (VC-dimension, PAC-Bayes, and others) are all related to the number of bits required to communicate the labels given the unlabeled data for a natural communication game. Motivated by this observation, we give a general sample complexity bound based on this game that allows us to unify these different bounds in one common framework.

Metadata
Title
PAC-MDL Bounds
Authors
Avrim Blum
John Langford
Copyright Year
2003
Publisher
Springer Berlin Heidelberg
Book
Learning Theory and Kernel Machines

Print ISBN: 978-3-540-40720-1

Electronic ISBN: 978-3-540-45167-9

Copyright Year: 2003

DOI
https://doi.org/10.1007/978-3-540-45167-9_26

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