Handwritten digit recognition with kernel-based LVQ classifier in input space

被引:0
|
作者
Lv, HR [1 ]
Wang, WY [1 ]
机构
[1] Tsing Hua Univ, Dept Automat, Beijing 100084, Peoples R China
关键词
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, a kemel-based LVQ classifier in input space is proposed to recognize handwritten digit. Classical Learning Vector Quantization is performed in the input space through Euclidean distance, but it doesn't work well when the input patterns are highly nonlinear. In our model, the kernel method is used to define a new metric of distance in input space so we can get a direct view of the clustering result. At last, we test our model by handwritten digit recognition using MNIST database and it obtains better recognition performance than traditional LVQ.
引用
收藏
页码:203 / 208
页数:6
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