Multistage building learning based on misclassification measure

摘要

In the areas of machine learning and pattern recognition, discriminative learning methods are well-known for giving better classification performance than the methods which estimate probabilistic distributions of data. In this paper, we propose anew framework of multi-stage classification based on the Minimum Classification Error / Generalized Probabilistic Descent learning which is one of the promising discriminative learning methods. The proposed method makes it possible to use misclassifieddata to improve the classification performance by incorporating the supplemental features to the original feature vector space.