使用多分类器的数字识别

Derdour Khedidja, Mouss Hayet
{"title":"使用多分类器的数字识别","authors":"Derdour Khedidja, Mouss Hayet","doi":"10.1109/ISPS.2015.7244996","DOIUrl":null,"url":null,"abstract":"The aim of this paper is to describe the combining of several classifiers to the recognition of printed digits using a novel approach to describe the digits by hybrid feature extraction. The study has been conducted using three different features computed from cavities, zonal extraction and retinal representation along with nine different classifiers, K-Nearest Neighbor - KNN - with different distance measure, Support Vector Machine - SVM -, decision tree, linear discriminant analysis - LDA -. Classifier combination is considered by Majority Voting method. Experimental tests carried on the multi-font and multi-size printed digits dataset.","PeriodicalId":165465,"journal":{"name":"2015 12th International Symposium on Programming and Systems (ISPS)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Digit recognition using multiple classifiers\",\"authors\":\"Derdour Khedidja, Mouss Hayet\",\"doi\":\"10.1109/ISPS.2015.7244996\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The aim of this paper is to describe the combining of several classifiers to the recognition of printed digits using a novel approach to describe the digits by hybrid feature extraction. The study has been conducted using three different features computed from cavities, zonal extraction and retinal representation along with nine different classifiers, K-Nearest Neighbor - KNN - with different distance measure, Support Vector Machine - SVM -, decision tree, linear discriminant analysis - LDA -. Classifier combination is considered by Majority Voting method. Experimental tests carried on the multi-font and multi-size printed digits dataset.\",\"PeriodicalId\":165465,\"journal\":{\"name\":\"2015 12th International Symposium on Programming and Systems (ISPS)\",\"volume\":\"3 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-04-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 12th International Symposium on Programming and Systems (ISPS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISPS.2015.7244996\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 12th International Symposium on Programming and Systems (ISPS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISPS.2015.7244996","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

摘要

本文的目的是描述几种分类器的组合来识别印刷数字,使用一种新的方法来描述数字混合特征提取。该研究使用了从空腔、区域提取和视网膜表征中计算出的三种不同特征,以及九种不同的分类器,即不同距离度量的k -最近邻- KNN,支持向量机- SVM -,决策树,线性判别分析- LDA -。分类器组合采用多数投票法。对多字体、多尺寸印刷数字数据集进行了实验测试。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Digit recognition using multiple classifiers
The aim of this paper is to describe the combining of several classifiers to the recognition of printed digits using a novel approach to describe the digits by hybrid feature extraction. The study has been conducted using three different features computed from cavities, zonal extraction and retinal representation along with nine different classifiers, K-Nearest Neighbor - KNN - with different distance measure, Support Vector Machine - SVM -, decision tree, linear discriminant analysis - LDA -. Classifier combination is considered by Majority Voting method. Experimental tests carried on the multi-font and multi-size printed digits dataset.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信