A Novel Least Square Twin Support Vector Regression

This paper proposes a new method for regression named lp norm least square twin support vector regression (PLSTSVR), which is formulated by the idea of twin support vector regression (TSVR). Different from TSVR, our new model is an adaptive learning procedure with p-norm SVM (0<p≤2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{0<p\le 2}}$$\end{document}), where p is viewed as an adjustable parameter and can be automatically chosen by data. An iterative algorithm is suggested to solve PLSTSVR efficiently. In each iteration, only a series systems of linear equations (LEs) are solved. Experiments carried out on several standard UCI datasets and synthetic datasets show the feasibility and effectiveness of the proposed method.