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
收藏
页码：483 / +
页数：2
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