A learning algorithm for source aggregation

被引：9

作者：

Papayiannis, Georgios I. ^{[1
]}

Yannacopoulos, Athanassios N. ^{[1
]}

机构：

[1] Athens Univ Econ & Business, Dept Stat, Athens, Greece

来源：

MATHEMATICAL METHODS IN THE APPLIED SCIENCES | 2018年 / 41卷 / 03期

关键词：

Wasserstein barycenter; weights selection; convex optimization;

D O I：

10.1002/mma.4086

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

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 (c) 2016 John Wiley & Sons, Ltd.

引用

页码：1033 / 1039

页数：7

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