Least Squares Support Vector Machines Based on Support Vector Degrees
A modified least squares support vector machines (LS-SVM) approach, which treats the training data points differently according to their different degrees of importance, is proposed in this paper. On each data point, a support vector degree is defined and it is associated with the corresponding absolute value of Lagrange multiplier. The experiment of identification of pH neutralization process with polluted measuring data is shown in this paper and the result indicates that the method is effective in identification of nonlinear system. By contrast with the basic LS-SVM, the result also shows the priority of the presented new algorithm.
KeywordsSupport Vector Machine Support Vector Lagrange Multiplier Little Square Support Vector Machine Little Square Support Vector Machine Model
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