Support Vector Machines for Regression: A Succinct Review of Large-Scale and Linear Programming Formulations

作     者：Pablo Rivas-Perea Juan Cota-Ruiz David Garcia Chaparro Jorge Arturo Perez Venzor Abel Quezada Carreón Jose Gerardo Rosiles 

作者机构：Department of Computer Science School of Engineering & Computer Science Baylor University Waco USA Department of Electrical & Computer Engineering Autonomous University of Ciudad Juárez Ciudad Juárez México Rosiles Consulting El Paso USA 

出 版 物：《International Journal of Intelligence Science》 (智能科学国际期刊（英文）)

年 卷 期：2013年第3卷第1期

页      面：5-14页

学科分类：081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Support Vector Machines Support Vector Regression Linear Programming Support Vector Regression 

摘      要：Support Vector-based learning methods are an important part of Computational Intelligence techniques. Recent efforts have been dealing with the problem of learning from very large datasets. This paper reviews the most commonly used formulations of support vector machines for regression (SVRs) aiming to emphasize its usability on large-scale applications. We review the general concept of support vector machines (SVMs), address the state-of-the-art on training methods SVMs, and explain the fundamental principle of SVRs. The most common learning methods for SVRs are introduced and linear programming-based SVR formulations are explained emphasizing its suitability for large-scale learning. Finally, this paper also discusses some open problems and current trends.