{"title":"基于Huber损失函数的鲁棒模糊变系数回归模型","authors":"A. Khammar, M. Arefi, M. Akbari","doi":"10.1109/CFIS49607.2020.9238742","DOIUrl":null,"url":null,"abstract":"A generalized fuzzy regression model named fuzzy varying coefficient regression model is proposed by Shen et al. [14]. In this study, we introduce a fuzzy varying coefficient regression model based on Huber loss function and a kernel function. Unlike Shen et al.'s approach, the our approach is robust in the presence of outliers data. This advantage is examined by a numerical example.","PeriodicalId":128323,"journal":{"name":"2020 8th Iranian Joint Congress on Fuzzy and intelligent Systems (CFIS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Robust fuzzy varying coefficient regression model based on Huber loss function\",\"authors\":\"A. Khammar, M. Arefi, M. Akbari\",\"doi\":\"10.1109/CFIS49607.2020.9238742\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A generalized fuzzy regression model named fuzzy varying coefficient regression model is proposed by Shen et al. [14]. In this study, we introduce a fuzzy varying coefficient regression model based on Huber loss function and a kernel function. Unlike Shen et al.'s approach, the our approach is robust in the presence of outliers data. This advantage is examined by a numerical example.\",\"PeriodicalId\":128323,\"journal\":{\"name\":\"2020 8th Iranian Joint Congress on Fuzzy and intelligent Systems (CFIS)\",\"volume\":\"11 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 8th Iranian Joint Congress on Fuzzy and intelligent Systems (CFIS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CFIS49607.2020.9238742\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 8th Iranian Joint Congress on Fuzzy and intelligent Systems (CFIS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CFIS49607.2020.9238742","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Robust fuzzy varying coefficient regression model based on Huber loss function
A generalized fuzzy regression model named fuzzy varying coefficient regression model is proposed by Shen et al. [14]. In this study, we introduce a fuzzy varying coefficient regression model based on Huber loss function and a kernel function. Unlike Shen et al.'s approach, the our approach is robust in the presence of outliers data. This advantage is examined by a numerical example.
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