支持向量机多类目标分类器的结构简化研究

由于支持向量机(SVM)在模式识别和回归分析中有着独特优势，因此成为近来研究的热点，其优势主要体现在处理非线性和高维数据问题方面。最初的SVM特别适合解决两类目标分类问题，而对于多类目标分类，则需将其转化为多个两类目标分类问题，相应地即可构造多个两类目标子分类器，但由于这种情况导致了分类器结构的过于复杂，从而导致判决速度的降低。为了快速地进行分类．提出了一种简化结构的多类目标分类器，其不仅使得子分类器数目大大减少，而且使分类速度明显提高；同时对其分类精度和复杂度进行了对比分析。实验结果证明。该分类器是有效的。

The Research of Simplification of Structure of Multi-class Classifier of Support Vector Machine

Because of the unique property in pattern recognition and in regression analysis, support vector machine(SVM) has become the topic of research recently. The advantages of SVM mainly lie in its capabilities of processing non-linear and highly dimensional data problems. Unextended SVM is very suitable for solving two-class classification problems. For multi-class classification, however, it should be converted into many of two-class classification problems, and can be constructed many of two-class classifier correspondingly. But the case results in more complexity of classifier structure, and so leads to decrease of decision speed. In order to get a fast classification, a new multi-class classifier with simplified structure is put forward so that the number of subclassifiers and decisive time are reduced greatly. The accuracy and complexity are also contrastively analyzed here. The validity of the new classifier is proved by simulated experiments.