A learning algorithm for source aggregation

The problem of model aggregation from various information sources of unknown validity is addressed in terms of a variational problem in the space of probability measures. A weight allocation scheme to the various sources is proposed, which is designed to lead to the best aggregate model compatible w...

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Published inMathematical methods in the applied sciences Vol. 41; no. 3; pp. 1033 - 1039
Main Authors Papayiannis, Georgios I., Yannacopoulos, Athanassios N.
Format Journal Article
LanguageEnglish
Published Freiburg Wiley Subscription Services, Inc 01.02.2018
Subjects
Agglomeration
convex optimization
Information sources
Machine learning
Wasserstein barycenter
weights selection
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ISSN0170-4214
1099-1476
DOI10.1002/mma.4086

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Summary:The problem of model aggregation from various information sources of unknown validity is addressed in terms of a variational problem in the space of probability measures. A weight allocation scheme to the various sources is proposed, which is designed to lead to the best aggregate model compatible with the available data and the set of prior measures provided by the information sources. Copyright © 2016 John Wiley & Sons, Ltd.
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ISSN:0170-4214
1099-1476
DOI:10.1002/mma.4086