Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4970023 | Pattern Recognition Letters | 2017 | 8 Pages |
Abstract
A number of studies on learning Bayesian networks have emphasized the importance of exploiting regularities in conditional probability distributions, i.e., local structure. In this paper, we encode local structures as linear combinations of Boolean functions. By using Lasso, we can simultaneously estimate the structure and parameters of the networks from limited data. We demonstrate that the method leads to improved performance in terms of structural correctness as well as prediction score even when the local structure in the underlying model is only implicit.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Yuan Zou, Johan Pensar, Teemu Roos,