In this paper, a modified version of the Classical Lagrange Multiplier method is developed for convex quadratic optimization problems. The method, which is evolved from the first order derivative test for optimality o...In this paper, a modified version of the Classical Lagrange Multiplier method is developed for convex quadratic optimization problems. The method, which is evolved from the first order derivative test for optimality of the Lagrangian function with respect to the primary variables of the problem, decomposes the solution process into two independent ones, in which the primary variables are solved for independently, and then the secondary variables, which are the Lagrange multipliers, are solved for, afterward. This is an innovation that leads to solving independently two simpler systems of equations involving the primary variables only, on one hand, and the secondary ones on the other. Solutions obtained for small sized problems (as preliminary test of the method) demonstrate that the new method is generally effective in producing the required solutions.展开更多
为提高网络入侵检测系统中检测算法的分类精度,降低训练样本及学习时间,在基于支持向量回归机的基础上,提出一种新的利用Lagrange支持向量回归机设计IDS的检测算法。使用KDD CUP 1999数据集进行仿真实验,结果表明该算法较基于支持向量...为提高网络入侵检测系统中检测算法的分类精度,降低训练样本及学习时间,在基于支持向量回归机的基础上,提出一种新的利用Lagrange支持向量回归机设计IDS的检测算法。使用KDD CUP 1999数据集进行仿真实验,结果表明该算法较基于支持向量回归机的检测算法具有更良好的泛化性能、更快的迭代速度、更高的检测精度和更低的误报率。展开更多
文摘In this paper, a modified version of the Classical Lagrange Multiplier method is developed for convex quadratic optimization problems. The method, which is evolved from the first order derivative test for optimality of the Lagrangian function with respect to the primary variables of the problem, decomposes the solution process into two independent ones, in which the primary variables are solved for independently, and then the secondary variables, which are the Lagrange multipliers, are solved for, afterward. This is an innovation that leads to solving independently two simpler systems of equations involving the primary variables only, on one hand, and the secondary ones on the other. Solutions obtained for small sized problems (as preliminary test of the method) demonstrate that the new method is generally effective in producing the required solutions.
文摘为提高网络入侵检测系统中检测算法的分类精度,降低训练样本及学习时间,在基于支持向量回归机的基础上,提出一种新的利用Lagrange支持向量回归机设计IDS的检测算法。使用KDD CUP 1999数据集进行仿真实验,结果表明该算法较基于支持向量回归机的检测算法具有更良好的泛化性能、更快的迭代速度、更高的检测精度和更低的误报率。
