A New Smooth Support Vector Regression Based on ε-Insensitive Logistic Loss Function

摘要

A new smooth support vector regression based on ε-insensitive logistic loss function, shortly Lε-SSVR, was proposed in this paper, which is similar to SSVR, but without adding any heuristic smoothing parameters and with robust absolute loss. Taking advantage of Lε-SSVR, one can now consider SVM as linear programming, and efficiently solve large-scale regression problems without any optimization packages. Details of this algorithm and its implementation were presented in this paper. Simulation results for both artificial and real data show remarkable improvement of generalization performance and training time.