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 th...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 GMM. An experimental example has shown its effectiveness. The experimental results have shown that the recognition accuracy derived by the approach is higher than those obtained by the Vector Quantization (VQ) approach, the Radial Basis Function (RBF) network model, the Learning Vector Quantization (LVQ) approach and the GMM. In addition, the training time of the approach is less than that of Multilayer Perceptron (MLP).展开更多
In this paper, a new covering algorithm called FCV1 is presented. FCV1 comprises two algorithms, one of which is able to fast search for a partial rule and exclude the larg portion of negative examples, the other algo...In this paper, a new covering algorithm called FCV1 is presented. FCV1 comprises two algorithms, one of which is able to fast search for a partial rule and exclude the larg portion of negative examples, the other algorithm incorporates the more optimized greedy set-covering algorithm, and runs on a small portion of training examples. Hence,the training process of FCV1 is much faster than that of AQ15.展开更多
文摘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 GMM. An experimental example has shown its effectiveness. The experimental results have shown that the recognition accuracy derived by the approach is higher than those obtained by the Vector Quantization (VQ) approach, the Radial Basis Function (RBF) network model, the Learning Vector Quantization (LVQ) approach and the GMM. In addition, the training time of the approach is less than that of Multilayer Perceptron (MLP).
文摘In this paper, a new covering algorithm called FCV1 is presented. FCV1 comprises two algorithms, one of which is able to fast search for a partial rule and exclude the larg portion of negative examples, the other algorithm incorporates the more optimized greedy set-covering algorithm, and runs on a small portion of training examples. Hence,the training process of FCV1 is much faster than that of AQ15.
