Recently, a minimum classification error training algorithm has been proposed for minimizing the misclassification probability based on a given set of training samples using a generalized probabilistic descent method. This algorithm is a type of discriminative learning algorithm, but it approaches the objective of minimum classification error in a more direct manner than the conventional discriminative training algorithms. We apply this algorithm for simultaneous design pf feature extractor and pattern classifier, and demonstrate some of its properties and advantages.
Bibliographic reference. Paliwal, Kuldip K. / Bacchiani, M. / Sagisaka, Yoshinori (1995): "Minimum classification error training algorithm for feature extractor and pattern classifier in speech recognition", In EUROSPEECH-1995, 541-544.