Support Vector Regression for the Prediction of Methylene Blue Adsorption of Bentonite

支持向量回归用于润土吸蓝量的预报

刘太昂(Tai-Ang Liu)

《Journal of Data Analysis》 2卷1期 (2007/02) Pp. 101-111

https://doi.org/10.6338/JDA.200702_2(1).0008

摘要

本文用新近提出的、特别适合于小样本多变量的支持向量回归（Support vector regression）方法建立润土的五个参数与吸蓝量之间关系的预报模型。用“留一法”检验模型的预报能力，并将结果与传统的方法（人工神经网络、多元线性回归）进行比较，结果表明：支持向量回归的预报准确率比人工神经网络和多元线性回归方法高。

關鍵字

支持向量回归 ； 润土 ； 定量预报

並列摘要

Support vector machine proposed by Vapnik is a newly developed technique for data mining. It is suitable for the data processing based on finite number of training samples, with special technique to restrict overfitting. In this work, support vector regression has been used for correlating and modeling the relationships between the parameters and methylene blue adsorption of Bentonite. The prediction accuracy of the model was discussed on the basis of the leave-one-out cross-validation. The results show that the prediction accuracy of SVR model was higher than those of back propagation artificial neural network (BP ANN), multiple linear regression (MLR) methods.

並列關鍵字

support vector regression ； Bentonite ； quantitative prediction

參考文獻

Li, Jin-e,Dai, Bin(2005).the Study of Using Bentonite of Xiazijie in Xinjiang to Prepare Bentonite.9th Chinese National Chemical Technology Annual Meeting.(9th Chinese National Chemical Technology Annual Meeting).

Sun, Xifang,Wu, Zhansheng(2005).Processing Technology of Combination with Purification and Modification of Bentonite from Xiazijie in Xinjiang.9th Chinese National Chemical Technology Annual Meeting.(9th Chinese National Chemical Technology Annual Meeting).

Vapnik, Vladimir N.(1998).Statistical Learning Theory.the USA:A Wiley-Interscience Publication, John Wiley and Sons, Inc..

Cortes, C.,Vapnik, V.(1995).Support vector networks.Machine Learning.20,273.

Burbidge, R.,Trotter, M.,Buxton, B.,Holden, S.(2001).Drug design by machine learning: support vector machines for pharmaceutical data analysis.Comput Chem..26(1),5-14.