【摘要】 基于 SVM理论的分类器已经发展成为一种通用的二值分类器 .但是它对噪音数据非常敏感 ,而且不适用于多值分类场合 .将标准的 PCA算法扩展到更普遍的领域 ,并提出了一种新的 SVM分类器学习结构 .它使用扩展的 PCA算法对训练集数据进行降噪映射 ,产生一个新的数据集 ,然后通过反对称阵将一组二值分类器组合成一个多值分类器来处理该数据集 .理论分析和试验表明该分类器学习效率高并具有很强的容噪性能更多还原

【Abstract】 Classifier based on SVM theory has been developed as a general purpose two class classifier. But it is very sensitive to noise and incapable of multi class classification. In this paper, the standard PCA is extended to the more general field. And a new SVM learning structure is proposed, which uses the extended PCA algorithm to map the training set to a new de noised data set, and then combines many two classifiers via dissymmetry matrix into a multi class classifier to deal with the data set. The theoretical analysis and experimental results show that this classifier is noise insensitive and of high learning efficiency.更多还原