Handwritten Digit Recognition Based on LS-SVM

被引：0

作者：

Zhao, Xiaoming ^{[1
]}

Zhang, Shiqing ^{[2
]}

机构：

[1] Taizhou Univ, Dept Comp Sci, Taizhou 318000, Peoples R China

[2] Taizhou Univ, Sch Phys & Elect Engn, Taizhou 318000, Peoples R China

来源：

ADVANCES IN FUTURE COMPUTER AND CONTROL SYSTEMS, VOL 1 | 2012年 / 159卷

关键词：

Handwritten digit recognition; Principal component analysis; Least squares support vector machines;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper we present a method of handwritten digit recognition based on least squares support vector machines (LS-SVM). Principal component analysis (PCA) is used to extract 10-dimension features from the original digit images for handwritten digit recognition. The performance of LS-SVM on handwritten digit recognition tasks is compared with three typical classification methods, including linear discriminant classifiers (LDC), the nearest neighbor (NN), and the back-propagation neural network (BPNN). The experimental results on the popular MNIST database indicate that LS-SVM obtains the best accuracy of 87.5% with 10-dimension features, outperforming the other used methods.

引用

页码：483 / +

页数：2

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