Estimation of the regularization parameter for support vector regression

Support vector machines use a regularization parameter C to regulate the trade-off between the complexity of the model and the empirical risk of the model. Most of the techniques available for determining the optimal value of C are very time consuming. For industrial applications of the SVM method, there is a need for a fast and robust method to estimate C. A method based on the characteristics of the kernel, the range of output values and the size of the /spl epsiv/-insensitive zone, is proposed.