A Robust Support Vector Regression Based on Fuzzy Clustering

Support Vector Regression (SVR) has been very successful in pattern recognition, text categorization and function approximation. In real application systems, data domain often suffers from noise and outliers. When there is noise and/or outliers existing in sampling data, the SVR may try to fit those improper data and obtained systems may have the phenomenon of overfitting. In addition, the memory space for storing the kernel matrix of SVR will be increment with O (N-2), where N is the number of training data. In this paper, a robust support vector regression is proposed for nonlinear function approximation problems with noise and outliers.