With kernel function of radial basis function (RBF), least squares support vector machines (LSSVM) is used for non-linear system prediction in this paper. For limitation of gridding search method of cross validation, the parameters optimizing method is proposed to determine the regularization parameter and the kernel width parameter of LSSVM. And the methodology steps of this method are presented in detail. Compared with gridding search method, the applicability is validated through simulation experiment. In addition to higher generalization performance, the prediction results of nonlinear system show that this method can achieve higher prediction precision and cost less modeling time than BPNN.
This paper introduces the decision tree binary classification algorithm to classify the gender of speech data. In speech this unstructured data, in order to recognize the gender of the input voice data by computer, it is necessary to extract the features of unstructured voice data into structured data that can be recognized by computer, and then use structured data and decision tree for binary classification to train the binary classification decision tree model. Finally, the binary classification model of decision tree is used to predict and identify the gender of structured speech data that need classification and recognition.
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