Abstract
We address in this paper one of the prominent problems in pattern recognition, namely minimization of classification/recognition error rate. We propose a unconventional approach and a new formulation of the problem aiming at directly achieving a minimum classification error performance. The approach is called discriminative training which differs from the traditional statistical pattern recognition approach in its objective. Unlike the Bayesianframework, the new method does not require estimation of prob-ability distributions which usually cannot be reliably obtained. The new method has been applied in various experimental studies with good results, some of which are high-lighted in the paper to demonstrate the effectiveness of the new method. A broad range of problems can benefit from this new formulation.
