{"title":"度量空间中的规则近似","authors":"L. Kovács","doi":"10.1109/SAMI.2010.5423702","DOIUrl":null,"url":null,"abstract":"The classical fuzzy rule interpolators work in Euclidean spaces where the new fuzzy value can be generated from the training values. The paper investigates the case when the fuzzy values are defined over the general metric spaces. In this case a classification process is used to approximate the requested value. The paper introduces a base method for this classification process.","PeriodicalId":306051,"journal":{"name":"2010 IEEE 8th International Symposium on Applied Machine Intelligence and Informatics (SAMI)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2010-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":"{\"title\":\"Rule approximation in metric spaces\",\"authors\":\"L. Kovács\",\"doi\":\"10.1109/SAMI.2010.5423702\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The classical fuzzy rule interpolators work in Euclidean spaces where the new fuzzy value can be generated from the training values. The paper investigates the case when the fuzzy values are defined over the general metric spaces. In this case a classification process is used to approximate the requested value. The paper introduces a base method for this classification process.\",\"PeriodicalId\":306051,\"journal\":{\"name\":\"2010 IEEE 8th International Symposium on Applied Machine Intelligence and Informatics (SAMI)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-03-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"13\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 IEEE 8th International Symposium on Applied Machine Intelligence and Informatics (SAMI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SAMI.2010.5423702\",\"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 IEEE 8th International Symposium on Applied Machine Intelligence and Informatics (SAMI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SAMI.2010.5423702","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The classical fuzzy rule interpolators work in Euclidean spaces where the new fuzzy value can be generated from the training values. The paper investigates the case when the fuzzy values are defined over the general metric spaces. In this case a classification process is used to approximate the requested value. The paper introduces a base method for this classification process.
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