{"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}
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.
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