发明名称 |
Input feature and kernel selection for support vector machine classification |
摘要 |
A feature selection technique for support vector machine (SVM) classification makes use of fast Newton method that suppresses input space features for a linear programming formulation of a linear SVM classifier, or suppresses kernel functions for a linear programming formulation of a nonlinear SVM classifier. The techniques may be implemented with a linear equation solver, without the need for specialized linear programming packages. The feature selection technique may be applicable to linear or nonlinear SVM classifiers. The technique may involve defining a linear programming formulation of a SVM classifier, solving an exterior penalty function of a dual of the linear programming formulation to produce a solution to the SVM classifier using a Newton method, and selecting an input set for the SVM classifier based on the solution.
|
申请公布号 |
US7421417(B2) |
申请公布日期 |
2008.09.02 |
申请号 |
US20030650121 |
申请日期 |
2003.08.28 |
申请人 |
WISCONSIN ALUMNI RESEARCH FOUNDATION |
发明人 |
MANGASARIAN OLVI L.;FUNG GLENN M. |
分类号 |
G06E1/00;G06F17/00;G06K9/62;G06N5/00 |
主分类号 |
G06E1/00 |
代理机构 |
|
代理人 |
|
主权项 |
|
地址 |
|