Discriminative Training of Chow-Liu tree Multinet Classifiers. Huang, Kaizhu Dept. of Computer Science and Engineering, CUHK. Outline. Background Classifiers Discriminative classifiers Generative classifiers Bayesian Multinet Classifiers Motivation
Discriminative Training of Chow-Liu tree Multinet Classifiers
Dept. of Computer Science and Engineering,
Example of Missing Information:
From left to right: Original digit, Cropped and resized digit, 50% missing digit, 75% missing digit, and occluded digit.
TJT: a generative model
HMM and GMM
Jaakkola and Haussler NIPS98
Difference: Our method performs a reverse process:
From Generative classifiers to Discriminative classifiers
Beaufays etc., ICASS99, Hastie etc., JRSS 96
Difference: Our method is designed for Bayesian Multinet Classifiers, a more general classifier.
for Class I
for Class 2
Estimate the distribution P1 to
approximate D1 accurately
Estimate the distribution P2 to
approximate D2 accurately
Use Bayes rule to
Comments: This framework discards the divergence information between classes.