A simpler approach to coefficient regularized support vector machines regression

We consider a kind of support vector machines regression (SVMR) algorithms associated with l q (1 ≤ q < ∞) coefficient-based regularization and data-dependent hypothesis space. Compared with former literature, we provide here a simpler convergence analysis for those algorithms. The novelty of our analysis lies in the estimation of the hypothesis error, which is implemented by setting a stepping stone between the coefficient regularized SVMR and the classical SVMR. An explicit learning rate is then derived under very mild conditions.