[1]吴瑞红,王亚丽,张环冲,等.一种基于最小二乘支持向量机的葡萄酒品质评判模型[J].华侨大学学报(自然科学版),2013,34(1):30-35.[doi:10.11830/ISSN.1000-5013.2013.01.0030]
 WU Rui-hong,WANG Ya-li,ZHANG Huan-chong,et al.An Evaluation Model of Wine Quality Based on Least Square Support Vector Machine[J].Journal of Huaqiao University(Natural Science),2013,34(1):30-35.[doi:10.11830/ISSN.1000-5013.2013.01.0030]
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一种基于最小二乘支持向量机的葡萄酒品质评判模型()
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《华侨大学学报(自然科学版)》[ISSN:1000-5013/CN:35-1079/N]

卷:
第34卷
期数:
2013年第1期
页码:
30-35
栏目:
出版日期:
2013-01-20

文章信息/Info

Title:
An Evaluation Model of Wine Quality Based on Least Square Support Vector Machine
文章编号:
1000-5013(2013)01-0030-06
作者:
吴瑞红 王亚丽 张环冲 王鲜芳
河南师范大学 计算机与信息技术学院, 河南 新乡 453003
Author(s):
WU Rui-hong WANG Ya-li ZHANG Huan-chong WANG Xian-fang
School of Computer and Information Technology, Henan Normal University, Xinxiang 453007, China
关键词:
最小二乘支持向量机 葡萄酒 多元分类器 交叉验证 品质评判
Keywords:
least square support vector machine wine multiple classifier cross validation quality evaluation
分类号:
TS262.6;TS207.3;TP183
DOI:
10.11830/ISSN.1000-5013.2013.01.0030
文献标志码:
A
摘要:
对源自UCI数据库的葡萄酒数据进行预处理,选取径向基函数作为最小二乘支持向量机的核函数;然后,根据“一对一”算法设计出最小二乘支持向量机多元分类器,并应用交叉验证算法对参数寻优,建立葡萄酒质量评判模型.同时,用BP神经网络、标准支持向量机分类器对葡萄酒进行训练.对比实验结果表明:最小二乘支持向量机比BP神经网络、标准支持向量机的平均分类准确率高,最高分类准确率为100%.
Abstract:
In this paper, the wine dataset from UCI databases is preprocessed and radial basis function is adopted as the kernel function of least square support vector machine(LS-SVM). And then a multi-classifier is designed from LS-SVM according to one-against-one algorithm. In addition, the cross-validation method is used to optimize parameters and the wine quality evaluation model is built. Meanwhile, LS-SVM is used in the wine quality evaluation and compared with the evaluation methodology based BP(back propagation)neural network and standard support vector machine.Simulation results show that the LS-SVM can achieve higher accuracy than BP neural network and standard support vector machine, with a highest 100% rate.

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备注/Memo

备注/Memo:
收稿日期: 2012-06-15
通信作者: 王鲜芳(1969-),女,教授,主要从事复杂过程建模与优化控制的研究.E-mail:xfwang11@yahoo.com.cn.
基金项目: 国家自然科学基金资助项目(61173071); 河南省科技攻关计划项目(112102210412); 河南省基础与前沿技术研究计划项目(112300410254); 河南省高校创新人才支持计划项目(2012HASTIT011)
更新日期/Last Update: 2013-01-20