The Supervised Learning Gaussian Mixture Model

The traditional Gaussian Mixture Model (GMM) for pattern recognition is an unsupervised learning method. The parameters in the model are derived only by the training samples in one class without taking into account the effect of sample distributions of other classes, hence, its recognition accuracy is not ideal sometimes. This paper introduces an approach for estimating the parameters in GMM in a supervising way.The Supervised Learning Gaussian Mixture Model (SLGMM) improves the recognition accuracy of the …