基于主成分分析的支持向量机模型对上海房价的预测研究  

Support Vector Machine Model Based on Principal Component Analysis for the Shanghai Real Estate Price of Prediction

作　　者：申瑞娜[1] 曹昶[1] 樊重俊[1] 

机构地区：[1]上海理工大学管理学院,上海200093

出　　处：《数学的实践与认识》2013年第23期11-16,共6页Mathematics in Practice and Theory

基　　金：上海市教育委员会科研创新重点资助项目(14ZZ131)

摘　　要：采用基于主成分分析的支持向量机方法对上海房价进行预测.首先利用主成分分析法对原始数据进行降维处理,然后利用具有高水平的小样本学习能力的支持向量机进行预测模型的建立,对上海房价进行预测.实证显示,经过主成分分析的支持向量机模型能够较好地处理复杂的房地产数据,具有较高的预测能力,为上海房地产业的发展提供参考.特别地,该模型可以普遍应用于影响因素众多,时效性较强的短期小样本数据问题的预测,具有较高的泛化能力和很好的预测精度.This paper uses the support vector machine model based on the principle com- ponent analysis to predict the house prices of Shanghai. Firstly, use the principle component analysis to reduce the dimension of the original data, form a new group of indexes that are linearly independent, then build a model using the ability of learning small sample of support vector machine（SVM^to predict the real estate prices. The result shows that the model built by the SVM based on the principle component analysis is of high forecasting ability, which can be used as a new method of house prices prediction.In addition, this model can be widely ap- plied to prediction of small samples which have many influence factors and strong timeliness,and has better generalization and prediction accuracy.

关 键 词：主成分分析 支持向量机 小样本 房价预测 

分 类 号：F293.3[经济管理—国民经济] F224