Linear dependency between ε and the input noise in ε-support vector regression
In using the /spl epsi/-support vector regression (/spl epsi/-SVR) algorithm, one has to decide a suitable value for the insensitivity parameter /spl epsi/. Smola et al. considered its "optimal" choice by studying the statistical efficiency in a location parameter estimation problem. While they successfully predicted a linear scaling between the optimal /spl epsi/ and the noise in the data, their theoretically optimal value does not have a close match with its experimentally observed counterpart in the case of Gaussian noise. In this paper, we attempt to better explain their experimental results by studying the regression problem itself. Our resultant predicted choice of /spl epsi/ is much closer to the experimentally observed optimal value, while again demonstrating a linear trend with the input noise.