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SVM is a novel statistical learning method that has been successfully applied in speaker recognition. However, Extractive feature vectors from the speech are overlapped and noisy is included in the original data space, these problems can lead to experience difficulties, training complication during training SVM, and the result will be reduced during the recognition phase. In this paper, a novel method...
We present a method for anisotropic fairing of point sets, which based on the covariance analysis and the efficiently constructed curvature. We use principal curvatures to detect features such as edges. Anisotropic curvature flow allows prominent surface features and other details of the objects to be treated in different ways. It is able to retain and even enhance important features such as edges...
A pedestrian detection method by using kernel principle component analysis (KPCA) and Fisher linear discriminant (FLD) is presented in this paper. The basic idea of this method is to first utilize the KPCA algorithm to perform feature extraction, which obtains the nonlinear principle components in the high dimension feature space composed of haar wavelet coefficients, and then implement classification...
In order to detect and diagnosis the exceptional signals, this paper presents a new computer aided test and diagnosis (CAT/CAD) model based on wavelet transform (WT) and support vector machine (SVM). The architecture of the CAT and CAD model and experimental feature extraction and pattern recognition by WT-SVM system are presented. The features of special frequency segment of the signal picked up...
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