新的稀疏支持向量回归机算法及实验研究  

Novel sparse Support Vector Regression and its experimental study

作　　者：陈晓峰[1] 王士同[1] 曹苏群[1,2] 马培勇[3] 

机构地区：[1]江南大学信息学院,江苏无锡214122 [2]淮阴工学院机械系,江苏淮安223001 [3]中国科学技术大学工程科学学院,合肥230026

出　　处：《计算机工程与应用》2008年第36期24-28,共5页Computer Engineering and Applications

基　　金：国家自然科学基金(No.60773206/F020106,No.60704047/F030304;国家高技术研究发展计划(863)(No.2007AA1Z158No.2006AA10Z313)~~

摘　　要：支持向量回归机是一种解决回归问题的重要方法,其预测速度与支持向量的稀疏性成正比。为了改进支持向量回归机的稀疏性,提出了一种直接稀疏支持向量回归算法DSKR(Direct Sparse Kernel Support Vector Regression),用于构造稀疏性支持向量回归机。DSKR算法对ε-SVR(ε-Support Vector Regression)增加一个非凸约束,通过迭代优化的方式,得到稀疏性好的支持向量回归机。在人工数据集和真实世界数据集上研究DSKR算法的性能,实验结果表明,DSKR算法可以通过调控支持向量的数目,提高支持向量回归机的稀疏性,且具有较好的鲁棒性。Support Vector Regression is an important kind of method for regression problems.The predicting speed of Support Vector Regression is proportional to its sparseness.In order to increase sparseness of support vector regression,in this paper,a sparse support vector regression method named DSKR （Direct Sparse Kernel Support Vector Regression） is proposed to construct sparse Support Vector Regression.DSKR adds a non-convex constraint to e-SVR （e-Support Vector Regression）,and then obtains Support Vector Regression with better sparseness using iterative optimization.Experimental comparisons are made with several Support Vector Regression methods on both synthetic data sets and real-world data sets,the comparisons show that the proposed DSKR gives promising results.It can improve sparseness and adjust number of support vectors with perfect robust performance.

关 键 词：支持向量回归机 核方法 稀疏核学习 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]