基于L-GEM的特征加权

2010 International Conference on Machine Learning and Cybernetics Pub Date : 2010-07-11 DOI:10.1109/ICMLC.2010.5581062

Qian-Cheng Wang, Wing W. Y. Ng, P. Chan, D. Yeung

{"title":"基于L-GEM的特征加权","authors":"Qian-Cheng Wang, Wing W. Y. Ng, P. Chan, D. Yeung","doi":"10.1109/ICMLC.2010.5581062","DOIUrl":null,"url":null,"abstract":"In this paper, we propose a novel method to weight features for their relevance to the given classification problem. The weight of a feature is computed by its Localized Generalization Error model (L-GEM). Then, a Radial Basis Function Neural Network (RBFNN) is trained by those weighted features. Experimental results on image classification problem show that the proposed method is efficient and effective in comparison to current methods.","PeriodicalId":126080,"journal":{"name":"2010 International Conference on Machine Learning and Cybernetics","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Feature weighting based on L-GEM\",\"authors\":\"Qian-Cheng Wang, Wing W. Y. Ng, P. Chan, D. Yeung\",\"doi\":\"10.1109/ICMLC.2010.5581062\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we propose a novel method to weight features for their relevance to the given classification problem. The weight of a feature is computed by its Localized Generalization Error model (L-GEM). Then, a Radial Basis Function Neural Network (RBFNN) is trained by those weighted features. Experimental results on image classification problem show that the proposed method is efficient and effective in comparison to current methods.\",\"PeriodicalId\":126080,\"journal\":{\"name\":\"2010 International Conference on Machine Learning and Cybernetics\",\"volume\":\"9 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-07-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 International Conference on Machine Learning and Cybernetics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICMLC.2010.5581062\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 International Conference on Machine Learning and Cybernetics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMLC.2010.5581062","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 5

摘要

在本文中，我们提出了一种新的方法来加权特征与给定分类问题的相关性。根据特征的局部泛化误差模型(L-GEM)计算特征的权重。然后，利用这些加权特征训练径向基函数神经网络(RBFNN)。在图像分类问题上的实验结果表明，与现有方法相比，该方法是有效的。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

Feature weighting based on L-GEM

In this paper, we propose a novel method to weight features for their relevance to the given classification problem. The weight of a feature is computed by its Localized Generalization Error model (L-GEM). Then, a Radial Basis Function Neural Network (RBFNN) is trained by those weighted features. Experimental results on image classification problem show that the proposed method is efficient and effective in comparison to current methods.

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

2010 International Conference on Machine Learning and Cybernetics

自引率

0.00%

发文量