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Minimizing Regret in Dynamic Decision Problems Read chapter Read first chapter

2015 | OriginalPaper | Chapter

Learning Contextual Discounting and Contextual Reinforcement from Labelled Data

Authors : David Mercier, Frédéric Pichon, Éric Lefèvre, François Delmotte

Published in: Symbolic and Quantitative Approaches to Reasoning with Uncertainty

Publisher: Springer International Publishing

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Abstract

This paper addresses the problems of learning from labelled data contextual discounting and contextual reinforcement, two correction schemes recently introduced in belief function theory. It shows that given a particular error criterion based on the plausibility function, for each of these two contextual correction schemes, there exists an optimal set of contexts that ensures the minimization of the criterion and that finding this minimum amounts to solving a constrained least-squares problem with as many unknowns as the domain size of the variable of interest.

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Appendix
Available only for authorised users
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Metadata
Title
Learning Contextual Discounting and Contextual Reinforcement from Labelled Data
Authors
David Mercier
Frédéric Pichon
Éric Lefèvre
François Delmotte
Copyright Year
2015
Book
Symbolic and Quantitative Approaches to Reasoning with Uncertainty

Print ISBN: 978-3-319-20806-0

Electronic ISBN: 978-3-319-20807-7

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-20807-7_43

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