Computer Engineering and Applications ›› 2007, Vol. 43 ›› Issue (19): 177-179.
• 数据库与信息处理 • Previous Articles Next Articles
ZHUANG Bin,MENG Zhi-qing
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庄 彬,孟志青
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Abstract: 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.
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。
ZHUANG Bin,MENG Zhi-qing. Forecasting method of temporal data based on support vector regress machine[J]. Computer Engineering and Applications, 2007, 43(19): 177-179.
庄 彬,孟志青. 基于支持向量机的时态数据预测方法[J]. 计算机工程与应用, 2007, 43(19): 177-179.
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