50 citations · 201 across the 20 of their papers we have counts for
4 papers · 1 filter
Information Theory and its Relation to Machine Learning
Bao-Gang Hu
In this position paper, I first describe a new perspective on machine learning (ML) by four basic problems (or levels), namely, "What to learn?", "How to learn?", "What to evaluate…
A New Approach of Deriving Bounds between Entropy and Error from Joint Distribution: Case Study for Binary Classifications
Bao-Gang Hu, Hong-Jie Xing
The existing upper and lower bounds between entropy and error are mostly derived through an inequality means without linking to joint distributions. In fact, from either theoretica…
Analytical Bounds between Entropy and Error Probability in Binary Classifications
Bao-Gang Hu, Hong-Jie Xing
The existing upper and lower bounds between entropy and error probability are mostly derived from the inequality of the entropy relations, which could introduce approximations into…
What are the Differences between Bayesian Classifiers and Mutual-Information Classifiers?
Bao-Gang Hu
In this study, both Bayesian classifiers and mutual information classifiers are examined for binary classifications with or without a reject option. The general decision rules in t…