摘要
支持向量回归机使用由经验误差项和常数项所构成的风险函数,满足结构风险最小原则。在时态数据预测领域,它将成为一种很有前途的预测方法。简要介绍了回归支持向量机的基本理论。基于回归支持向量机模型,建立了一个对时态数据预测的方法,可以对多属性时态数据进行预测,并与其它预测模型(BP神经网络)进行比较。实验结果表明所提出的方法在预测的稳定性和准确性方面都要优于BP神经网络模型。
Support Vector Regress machine（SVR） will be a promising method in temporal data forecasting fields because it uses a risk function consisting of the empirical error and a regularized term which is derived from the structural risk minimization principle.This paper briefly introduces the basic theory of Support Vector Regress （SVR） and applies SVR to create a model, which also can be used for forecasting the multi-attribute temporal data and the temporal data.The result of simulation shows that SVR is superior to BP Neutral Network in the stability and accuracy.
出处
《计算机工程与应用》
CSCD
北大核心
2007年第19期177-179,184,共4页
Computer Engineering and Applications
关键词
回归支持向量机
时态数据
预测
Support Vector Regress Machine
temporal data
forecasting
