摘要
提出了一种新的基于分类的SVM非线性回归算法(CSVR),首先将Y扩展为Y+ε和Y-ε两个数据集,再将n维输入空间X中的数据连同Y+ε和Y-ε组成n+1维空间χ中的两类数据,并用Z∈{+1,-1}来标识两类数据,再利用标准的SVM二分类算法求解。利用该算法对一系列的基准函数进行测试,取得了令人满意的结果。该算法对噪声数据不敏感,具有较好的鲁棒性,并且可以根据实际需要设定ε的大小,防止出现过拟合现象。该算法由于不需要先验地建立一个参数未知的回归模型,因此可以用在其他传统统计回归算法失效的场合。
A novel SVM nonlinear regression algorithm based on classification （CSVR） is proposed to solve the difficult problem of obtaining nonlinear regression function Y =f（X） under the condition of having no knowledge about regression model. First, Y is extended into two data sets, Y ＋ ε and Y- ε. Then two n＋1 dimension data sets labeled with Z∈ （＋1, -1 ） are achieved by adding n-dimension data of input space X to those two data sets. Next the two nonlinear unclassified data sets are transformed to linear classified data sets in a higher dimension feature space by a particular mapping function Ф.Finally, the paper trains the data sets with standard SVM two classification algorithm. Experiment is conducted on a series of benchmark functions and the result shows that the approach is satisfactory. This algorithm is robust and insensitive to noise. It can prevents from over-fitting by setting the value of ε empirically. Since the algorithm does not require a prior regression model with unknown variables, it can be utilized in the circumstances where other traditional statistical regression algorithms fail.
出处
《计算机工程》
EI
CAS
CSCD
北大核心
2005年第20期19-21,共3页
Computer Engineering
基金
国家自然科学基金资助项目(30271048)
校引进(留学)人才基金资助项目(G2002-28)
校科研基金资助项目(X02-070-1(Z))
作者简介
业宁（1967-），男，讲师、博士生，主研方向：机器学习和数据挖掘，E-mail：yening@njfu．edu．cn
梁作鹏，博士生
董逸生，教授、博导
王厚立，教授、博导
