Twin maximum entropy discriminations for classification

摘要

Maximum entropy discrimination (MED) is an excellent classification method based on the maximum entropy and maximum margin principles, and can produce hard-margin support vector machines (SVMs) under certain condition. In this paper, we propose a novel maximum entropy discrimination classifier called twin maximum entropy discriminations (TMED) which construct two discrimination functions for two classes such that each discrimination function is closer to one of the two classes and is at least γt distance from the other. Therefore, it is more flexible and has better generalization ability than typical MED. Furthermore, it solves a pair of convex optimization problems and has the same advantages as those of non-parallel SVM (NPSVM) which is only the special case of our TMED when the priors and parameters are chosen appropriately. It also owns the inherent sparseness as MED. Experimental results confirm the effectiveness of our proposed method.