Convergence rate of SVM for kernel-based robust regression
Convergence rate of SVM for kernel-based robust regression is a scholarly work, published in 2019 in ''International Journal of Wavelets, Multiresolution and Information Processing''. The main subjects of the publication include convergence, rate of convergence, outlier, kernel, quasiconvex function, multicollinearity, Robust regression, convex optimization, mathematical optimization, support vector machine, convex analysis, regular polygon, compressed sensing, applied mathematics, mathematics, regression, and computer science. It is known that to alleviate the performance deterioration caused by the outliers, the robust support vector (SV) regression is proposed, which is essentially a convex optimization problem associated with a non-convex loss function.