{"title":"鲁棒模式识别的模糊目标函数","authors":"Tai-Ning Yang, Chih-Jen Lee, Shi-Jim Yen","doi":"10.1109/FUZZY.2009.5277269","DOIUrl":null,"url":null,"abstract":"In this paper, we consider the issue of fuzzy objective functions when outliers exist. The outlier set is defined as the complement of the data set. Following this concept, a specially designed fuzzy membership weighted objective function is proposed and the corresponding optimal membership is derived. Based on the proposed robust objective functions, algorithms for clustering are implemented. Artificially generated data are used for comparison.","PeriodicalId":117895,"journal":{"name":"2009 IEEE International Conference on Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2009-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Fuzzy objective functions for robust pattern recognition\",\"authors\":\"Tai-Ning Yang, Chih-Jen Lee, Shi-Jim Yen\",\"doi\":\"10.1109/FUZZY.2009.5277269\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we consider the issue of fuzzy objective functions when outliers exist. The outlier set is defined as the complement of the data set. Following this concept, a specially designed fuzzy membership weighted objective function is proposed and the corresponding optimal membership is derived. Based on the proposed robust objective functions, algorithms for clustering are implemented. Artificially generated data are used for comparison.\",\"PeriodicalId\":117895,\"journal\":{\"name\":\"2009 IEEE International Conference on Fuzzy Systems\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-10-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 IEEE International Conference on Fuzzy Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/FUZZY.2009.5277269\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 IEEE International Conference on Fuzzy Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FUZZY.2009.5277269","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Fuzzy objective functions for robust pattern recognition
In this paper, we consider the issue of fuzzy objective functions when outliers exist. The outlier set is defined as the complement of the data set. Following this concept, a specially designed fuzzy membership weighted objective function is proposed and the corresponding optimal membership is derived. Based on the proposed robust objective functions, algorithms for clustering are implemented. Artificially generated data are used for comparison.
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