Handwritten Digit Recognition Based on Principal Component Analysis and Support Vector Machines

被引：0
|
作者：
Li, Rui [1 ]
Zhang, Shiqing [1 ]
机构：
[1] Taizhou Univ, Sch Phys & Elect Engn, Taizhou 318000, Peoples R China
来源：
ADVANCES IN COMPUTER SCIENCE, ENVIRONMENT, ECOINFORMATICS, AND EDUCATION, PT I | 2011年 / 214卷
关键词：
Handwritten digits recognition; Principal component analysis; Support vector machines;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Handwritten digit recognition has always been a challenging task in pattern recognition area. In this paper we explore the performance of support vector machines (SVM) and principal component analysis (PCA) on handwritten digits recognition. The performance of SVM on handwritten digits recognition task is compared with three typical classification methods, i.e., linear discriminant classifiers (LDC), the nearest neighbor (1-NN), and the back-propagation neural network (BPNN). The experimental results on the popular MNIST database indicate that SVM gets the best performance with an accuracy of 89.7% with 10-dimensional embedded features, outperforming the other used methods.
引用
收藏
页码：595 / 599
页数：5
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