A Generalization of the Chow-Liu Algorithm and its Application to Statistical Learning
Information Theory
2010-02-12 v1 Artificial Intelligence
Machine Learning
math.IT
Abstract
We extend the Chow-Liu algorithm for general random variables while the previous versions only considered finite cases. In particular, this paper applies the generalization to Suzuki's learning algorithm that generates from data forests rather than trees based on the minimum description length by balancing the fitness of the data to the forest and the simplicity of the forest. As a result, we successfully obtain an algorithm when both of the Gaussian and finite random variables are present.
Keywords
Cite
@article{arxiv.1002.2240,
title = {A Generalization of the Chow-Liu Algorithm and its Application to Statistical Learning},
author = {Joe Suzuki},
journal= {arXiv preprint arXiv:1002.2240},
year = {2010}
}