convex programming Problem

The key idea of SSVM is to transform the standard model of SVM into an unconstraint quadric convex programming problem.

SSVM模型的基本思想是将标准的支撑向量机模型转化成一个无约束二次凸规划模型进行求解。

The paper offers a dual problem for the semi-infinite convex programming by using the directional derivative with zero dual gap.

本文对半无限凸规划提出一个用方向导数表述的对偶问题，其对偶间隙为零。

SVM transforms machine learning to solve an optimization problem, and to solve a convex quadratic programming problem by the optimization theory and method constructing algorithms.

它将机器学习问题转化为求解最优化问题，并应用最优化理论构造算法来解决凸二次规划问题。