Robust classification and regression using support vector machines [An article from: European Journal of Operational Research]

This digital document is a journal article from European Journal of Operational Research, published by Elsevier in 2006. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.

Description:
In this paper, we investigate the theoretical aspects of robust classification and robust regression using support vector machines. Given training data (x"1,y"1),...,(x"l,y"l), where l represents the number of samples, x"i@?R^n and y"i@?{-1,1} (for classification) or y"i@?R (for regression), we investigate the training of a support vector machine in the case where bounded perturbation is added to the value of the input x"i@?R^n. We consider both cases where our training data are either linearly separable and nonlinearly separable respectively. We show that we can perform robust classification or regression by using linear or second order cone programming.