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The problem of probability density estimation can be used in many areas in signal processing, such as regression and classification. In this paper, a density estimation approach based on support vector machine (SVM) was developed. Our algorithm has robust results and sparse solutions compared with Parzen's method. Besides, we used fundamental splines instead of Gaussian kernels in order to further...
In this paper, we investigate two important issues that influence dialect classification: (i) exploring dialect dependent features, and (ii) an effective way of combining spectral, excitation, and vocal tract information to improve dialect classification. The motivation is that dialect dependent features such as formants, LSP (line spectral pairs) and MEPZ (MFCCs + energy + pitch) span a wider range...
In this study we have investigated the classification of old myocardial infarction through the analysis of 192 lead body surface potential maps (BSPM). Following an analysis of the most prominent features based on a signal to noise ratio ranking criterion the top 6 features were selected. These features were subsequently used as inputs to a series of supervised classification models in the form of...
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