摘要
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。
A complementarity support vector machine was obtained which is based on a ammended 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 computa- tional work. And the shortcoming of Lagrangian method proposed by Mangasarian et al. , which need compute the in- verse matrix that is not adapted to handle nonlinear large-scale classification problems,is overcomed. Furthermore,with- out any assumption, the global convegence is proved. Numerical experiments show that the algorithm is feasible and effective.
出处
《计算机科学》
CSCD
北大核心
2010年第2期165-166,206,共3页
Computer Science
基金
国家自然科学基金(项目编号60674108)资助
关键词
支持向量机
互补问题
下降算法
全局收敛
Support vector machines,Complementarity problem,Descent method,Global convergence
