{"title":"利用马氏距离研究先进的考虑方差的机器","authors":"Junheong Park, K. Sim, Seung-Min Park","doi":"10.1109/ICNC.2011.6022100","DOIUrl":null,"url":null,"abstract":"Support Vector Machine maximizes a margin between two groups. Variance-considered machine improves SVM to align hyper plane according to two classes' variance and prior probability to reduce the error rate. There is probabilistically imprecise things those data classified by VCM. In this paper, we introduce the VCM and try to propose a concept that is to confer reliability estimated by Mahalanobis distance upon data separated by VCM.","PeriodicalId":299503,"journal":{"name":"2011 Seventh International Conference on Natural Computation","volume":"11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Notice of RetractionStudy on advanced variance-considered machines using Mahalanobis distance\",\"authors\":\"Junheong Park, K. Sim, Seung-Min Park\",\"doi\":\"10.1109/ICNC.2011.6022100\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Support Vector Machine maximizes a margin between two groups. Variance-considered machine improves SVM to align hyper plane according to two classes' variance and prior probability to reduce the error rate. There is probabilistically imprecise things those data classified by VCM. In this paper, we introduce the VCM and try to propose a concept that is to confer reliability estimated by Mahalanobis distance upon data separated by VCM.\",\"PeriodicalId\":299503,\"journal\":{\"name\":\"2011 Seventh International Conference on Natural Computation\",\"volume\":\"11 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-07-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 Seventh International Conference on Natural Computation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICNC.2011.6022100\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 Seventh International Conference on Natural Computation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICNC.2011.6022100","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Notice of RetractionStudy on advanced variance-considered machines using Mahalanobis distance
Support Vector Machine maximizes a margin between two groups. Variance-considered machine improves SVM to align hyper plane according to two classes' variance and prior probability to reduce the error rate. There is probabilistically imprecise things those data classified by VCM. In this paper, we introduce the VCM and try to propose a concept that is to confer reliability estimated by Mahalanobis distance upon data separated by VCM.
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