Linear dependency between epsilon and the input noise in epsilon-support vector regression

James T. Kwok, Ivor W. Tsang

Abstract: In using the epsilon-support vector regression (epsilon-SVR) algorithm, one has to decide a suitable value for the insensitivity parameter epsilon. 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 epsilon 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 epsilon is much closer to the experimentally observed optimal value, while again demonstrating a linear trend with the input noise.

IEEE Transactions on Neural Networks, 14(3):544-553, May 2003.


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