PCA和支持向量机在财务分析中的应用

提出了基于主成分分析（PCA）的支持向量机分类原理，并应用于财务指标的分析研究。主要思想是先将训练样本和测试样本的属性集进行标准化处理，计算各属性间的相关程度，构造新属性集，然后使用支持向量分类机对训练样本进行训练，构造决策函数，最后将决策函数对测试样本进行测试。本文选择我国2000年末106家上市公司的主要财务指标进行经营状况的分类测试，实验结果表明，使用本文的分类模型可提高模式识别的准确度。

Research on SVM in Financing Analysis on PCA

This paper puts forward the principle of SVM classification based on Principal Components Analysis,and applies it to the research on the financing analysis.The main idea is to normalize the attributes of the sample for training and testing and to calculate the degree of correlation of different attributes at first,then to build up new attribute sets and apply them to SVM for classification.This paper has tested the two classification patterns for 106 companies listed on China Stock Exchange by 2000 and the results indicate that it can improve the accuracy on the recognition of model.