DNA classification based on Least Square Wavelet Support Vector Machine

doi:10.3778/j.issn.1002-8331.2009.12.071

Computer Engineering and Applications ›› 2009, Vol. 45 ›› Issue (12): 222-225.DOI: 10.3778/j.issn.1002-8331.2009.12.071

• 工程与应用 • Previous Articles Next Articles

DNA classification based on Least Square Wavelet Support Vector Machine

XIAN Guang-ming¹,ZENG Bi-qing¹,XIAN Guang-lin²

1.Computer Engineering Department of Nanhai Campus，South China Normal University，Foshan，Guangdong 528225，China
2.Computer Engineering Department of Guangdong Industry Technical College，Guangzhou 510300，China

Received:2008-03-07 Revised:2008-07-14 Online:2009-04-21 Published:2009-04-21
Contact: XIAN Guang-ming

最小二乘小波支持向量机的DNA序列分类方法

冼广铭¹,曾碧卿¹,冼广淋²

1.华南师范大学南海校区计算机工程系，广东佛山 528225
2.广东轻工职业技术学院计算机系，广州 510300

通讯作者: 冼广铭

Abstract

Abstract: SVM kernel function used at present can not approach to any classification boundary in sub-space of a square integrable space L₂（R）.Aiming at the problem above，based on conditions of the support vector kernel function and wavelet frame theory，authors propose the construction model of Least Square Wavelet Support Vector Machine（LS-WSVM）.Compared with standard SVM and LS-SVM under the same conditions，experimental results show that LS-WSVM has more excellent performance of feature abstraction in classification.

Key words: Support Vector Machine（SVM）, kernel function, Least Square Wavelet Support Vector Machine（LS-WSVM）, classification

摘要： 目前使用的已有SVM核函数，在分类中不能逼近某一L₂（R）（平方可积空间）子空间上的任意分类界面。针对上述问题，在支持向量机的核函数方法和小波框架理论的基础上，提出了LS-WSVM结构模型。实验结果表明，和标准的SVM和LS-SVM比较起来，在同等条件下，LS-WSVM在分类方面具有优良的特征提取性能。

关键词: 支持向量机, 核函数, 最小二乘小波支持向量机, 分类

XIAN Guang-ming¹,ZENG Bi-qing¹,XIAN Guang-lin². DNA classification based on Least Square Wavelet Support Vector Machine[J]. Computer Engineering and Applications, 2009, 45(12): 222-225.

冼广铭¹,曾碧卿¹,冼广淋². 最小二乘小波支持向量机的DNA序列分类方法[J]. 计算机工程与应用, 2009, 45(12): 222-225.

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DNA classification based on Least Square Wavelet Support Vector Machine

最小二乘小波支持向量机的DNA序列分类方法

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