Non-flat function estimation with a multi-scale support vector regression

Estimating the non-flat function which comprises both the steep variations and the smooth variations is a hard problem. The results achieved by the common support vector methods like SVR, LPR and LS-SVM are often unsatisfactory, because they cannot avoid underfitting and overfitting simultaneously. This paper takes this problem as a linear regression in a combined feature space which is implicitly defined by a set of translation invariant kernels with different scales, and proposes a multi-scale support vector regression (MS-SVR) method. MS-SVR performs better than SVR, LPR and LS-SVM in the experiments tried.