Hee-Joong Kang

Learn More
The performance of multiple classifier systems varies with the performance of component classifiers as well as the method of combination. In this paper, information-theoretic methods are proposed for constructing multiple classifier systems, provided that the number of component classifiers is constrained in advance. These proposed methods are applied to a(More)
Without an independence assumption, combining multiple classifiers deals with a high order probability distribution composed of classifiers and a class label. Storing and estimating the high order probability distribution is exponentially complex and unmanageable in theoretical analysis , so we rely on an approximation scheme using the dependency. In this(More)
In order to raise a class discrimination power by the combination of multiple classifiers, the upper bound of Bayes error rate which is bounded by the conditional entropy of a class and decisions should be minimized. Based on the minimization of the upper bound of the Bayes error rate, Wang and Wong proposed only a tree dependence approximation scheme of a(More)