中文摘要：

在K-SVCR算法结构的基础上构造了新的模型.模型的特点是它的一阶最优化条件可以转化为一个线性互补问题,通过Lagrangian隐含数,可以将其进一步转化成一个强凸的无约束优化问题.利用共轭梯度技术对其进行求解,在有限步内得到分类超平面.最后在标准数据集进行了初步试验.试验结果显示了提出的算法在分类的精度和速度上都有明显提高.

英文摘要：

In this paper,we start with a new formulation which is proposed based on the K-SVCR method.We then transform it as a complementarity problem and further a strongly convex unconstrained optimization problem by using the implicit Lagrangian function.Then the conjugate gradient algorithm with global and finite termination properties is established for solving the resulting optimization problem.This indicates that the algorithm can be implemented efficiently in practice.Preliminary numerical experiments on benchmark datasets show that the algorithm has good performance on both accuracy and training speed.