HANDWRITTEN DIGIT RECOGNITION USING AN OPTIMIZED NEAREST-NEIGHBOR CLASSIFIER

被引：24
|
作者：
YAN, H
机构：
[1] Department of Electrical Engineering, University of Sydney
来源：
PATTERN RECOGNITION LETTERS | 1994年 / 15卷 / 02期
基金：
澳大利亚研究理事会;
关键词：
NEAREST NEIGHBOR CLASSIFIER; PROTOTYPE OPTIMIZATION; MULTILAYER NEURAL NETWORK; HANDWRITTEN CHARACTER RECOGNITION;
D O I：
10.1016/0167-8655(94)90050-7
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 for handwritten digit recognition using a nearest neighbor classifier. In this method a set of prototypes are obtained from the training samples and used to build a nearest neighbor classifier. The classifier is then mapped to a multi-layer perceptron. After training the neural network is mapped back to a nearest neighbor classifier with new and optimized prototypes. Using this method we were able to obtain recognition accuracy from 93.7% with 100 prototypes to 96.2% with 500 prototypes for samples not used for training.
引用
收藏
页码：207 / 211
页数：5
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