为支持向量机器(SVM ) 的优化问题的模型被提供,它基于在一个点和它的设计之间的双标准和距离的定义到一架给定的飞机上。改进支持向量机器的模型基于 1 标准(1 - SVM ) 从优化问题被提供,还它是分离编程。与变光滑的技术和 optimality 知识,分离编程被变成连续编程。算法是,这个方法能选择并且压制这个问题容易的实现的试验性的结果表演展示更多的 efficiently.Illustrative 例子表演 1 - SVM 处理线性或非线性的分类很好。
The model of optimization problem for Support Vector Machine(SVM) is provided, which based on the definitions of the dual norm and the distance between a point and its projection onto a given plane. The model of improved Support Vector Machine based on 1-norm (1 - SVM) is provided from the optimization problem, yet it is a discrete programming. With the smoothing technique and optimality knowledge, the discrete programming is changed into a continuous programming. Experimental results show that the algorithm is easy to implement and this method can select and suppress the problem features more efficiently.Illustrative examples show that the 1 - SVM deal with the linear or nonlinear classification well.