A new constrained maximum margin approach to discriminative learning of Bayesian classifiers
We propose a novel discriminative learning approach for Bayesian pattern classification, called ‘constrained maximum
margin (CMM)’. We define the margin between two classes as the difference between the minimum decision value for positive
samples and the maximum decision value for negative samples. The learning problem is to maximize the margin under the con-
straint that each training pattern is classified correctly. This nonlinear programming problem is solved using the sequential un-
constrained minimization technique. We applied the proposed CMM approach to learn Bayesian classifiers based on Gaussian
mixture models, and conducted the experiments on 10 UCI datasets. The performance of our approach was compared with those of
the expectation-maximization algorithm, the support vector machine, and other state-of-the-art approaches. The experimental
results demonstrated the effectiveness of our approach.
关键词：
Discriminative learning,
Statistical modeling,
Bayesian pattern classifiers,
Gaussian mixture models,
UCI datasets
