Abstract: Support Vector Machine (SVM) is to correctly classify samples into two parallel planes in input or feature space by optimal planes (lines). And the margin between the two classes is made to be the largest. The standard SVM requires to solve quadratic program that needs considerable computational time. Based on a concrete decision support system of medical images, a novel algorithm is introduced to solve the problem. Experimental results on UCI and a developed decision support system demonstrate that the presented algorithm is simple, feasible, and faster with better precision.

Key words: Support Vector Machine(SVM), classification algorithm, decision support

摘要： 支持向量机(SVM)方法是利用最优分类面(线)将两类样本在特征空间或输入空间中无错误地分开，而且要使两类的分类空隙最大。因此标准的SVM方法需要求解二次规划问题，计算量很大。该文以一个医学决策支持系统为应用背景，介绍一种解决该问题的新方法。在UCI数据集和所开发的决策支持系统上的应用表明，该算法简便可行，具有更高的精度和更快的速度。

关键词: 支持向量机, 分类算法, 决策支持

CLC Number: 

• TP391