CAJ | 학술논문

基于支持向量机的修正模型.得到一个互补支持向量机.利用Fischer-Burmeister互补函数,提出了一个新的下降算法.该算法不是基于支持向量机最优化问题本身,而是一个与之等价的互补问题.新算法不需要计算任何Hesse矩阵或矩阵求逆运算,实现简单,计算量小,克服了Mangasarian等人提出的LSVM算法需要求逆矩阵而造成不适合求解大规模非线性分类问题的缺陷.在不需要任何假设的情况下,证明了算法的全局收敛性.仿真实验表明算法是可行有效的.
기우지지향량궤적수정모형.득도일개호보지지향량궤.이용Fischer-Burmeister호보함수,제출료일개신적하강산법.해산법불시기우지지향량궤최우화문제본신,이시일개여지등개적호보문제.신산법불수요계산임하Hesse구진혹구진구역운산,실현간단,계산량소,극복료Mangasarian등인제출적LSVM산법수요구역구진이조성불괄합구해대규모비선성분류문제적결함.재불수요임하가설적정황하,증명료산법적전국수렴성.방진실험표명산법시가행유효적.
A complementarity support vector machine was obtained which is based on a arnmended problem of surpport vector machine.By using Fischer-Burmeister function,a new descent algorithm for support vector machine optimization problem was presented.The proposed algorithm does not base on the primal quadratic programming problem of SVM,but a complementarity problem.It mustn't compute any Hesse or the inverse matrix with simple and small computational work.And the shortcoming of Lagrangian method proposed by Mangasarian et al.,which need compute the inverse matrix that is not adapted to handle nonlinear large-scale classification problems,is overcome&Furthermore.without any assumption,the global convegence is proved.Numerical experiments show that the algorithm is feasible and effective.