计算机工程与应用2011，Vol.47Issue(33)：52-54,3.DOI:10.3778/j.issn.1002-8331.2011.33.015
广义支持向量机的多项式光滑函数法
Polynomial smooth functions method for generalized support vector machine
摘要
Abstract
To solve the optimization problem of Generalized Support Vector Machine (GSVM),the primal optimization problem' with inequality constraints is transformed into the unconstraint optimization problem, whose objective function is nonsmooth, therefore a series of polynomial smooth functions is introduced to approach the objective function.Different polynomial functions can be used according to the corresponding accuracy demand.The model is solved by the BFGS algorithm.Experimental results show,compared with the existing algorithms used for solving the optimization problem of GSVM,the proposed algorithm achieves higher testing accuracy more quickly and is useful for large-scale data.Therefore,the proposed algorithm is effective.关键词
支持向量机/广义支持向量机/模式识别/分类/光滑函数/多项式Key words
Support Vector Machine (SVM)/generalized support vector machines/pattern recognition/classification/smooth function/polynomial分类
信息技术与安全科学引用本文复制引用
刘叶青,刘三阳,谷明涛..广义支持向量机的多项式光滑函数法[J].计算机工程与应用,2011,47(33):52-54,3.基金项目
国家自然科学基金(the National Natural Science Foundation of China under Grant No.60705004) （the National Natural Science Foundation of China under Grant No.60705004）
河南科技大学博士科研启动基金资助. （）
