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 of feature extractor and pattern classifier, and demonstrate some of its properties and advantages.
|Published - 1995
|Proceedings of the 5th IEEE Workshop on Neural Networks for Signal Processing (NNSP'95) - Cambridge, MA, USA
継続期間: 1995 8月 31 → 1995 9月 2
|Proceedings of the 5th IEEE Workshop on Neural Networks for Signal Processing (NNSP'95)
|Cambridge, MA, USA
|95/8/31 → 95/9/2
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