Linear Dependency betweenand the Input Noise in -Support Vector Regression
In using the � -support vector regression (� -SVR) algorithm, one has to decide on a suitable value of the insensitivity parameter � . Smola et al. (6) determined its "optimal" choice based on maximizing the statistical efficiency of a location parameter estimator. While they successfully predicted a linear scaling between the optimaland the noise in the data, the value of the theoretically optimaldoes not have a close match with its experimentally observed counterpart. In this paper, we attempt to better explain the experimental results there, by analyzing a toy problem with a closer setting to the � -SVR. Our resultant predicted choice ofis much closer to the experimentally observed value, while still demonstrating a linear trend with the data noise.