Learning parameters in canonical models using weighted least squares

被引：0

作者：

Nowak, Krzysztof ^{[1
,3
]}

Druzdzel, Marek J. ^{[1
,2
]}

机构：

[1] Bialystok University of Technology, Białystok

[2] School of Information Sciences, Pittsburgh

[3] European Space Agency, Noordwijk

来源：

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | 2014年 / 8754卷

基金：

美国国家卫生研究院;

关键词：

Bayesian networks; canonical models; noisy–MAX gates; parameter learning; weighted least squares;

D O I：

10.1007/978-3-319-11433-0_24

中图分类号：

学科分类号：

摘要：

We propose a novel approach to learning parameters of canonical models from small data sets using a concept employed in regression analysis: weighted least squares method. We assess the performance of our method experimentally and show that it typically outperforms simple methods used in the literature in terms of accuracy of the learned conditional probability distributions. © 2014 Springer International Publishing Switzerland.

引用

页码：366 / 381

页数：15

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