Abstract
We consider a kind of support vector machines regression (SVMR) algorithms associated with 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.
Citation
Hongzhi Tong. Di-Rong Chen. Fenghong Yang. "A Simpler Approach to Coefficient Regularized Support Vector Machines Regression." Abstr. Appl. Anal. 2014 1 - 8, 2014. https://doi.org/10.1155/2014/206015