{"title":"一个简单的Minkowski度量分类器","authors":"G. Toussaint","doi":"10.1109/TSSC.1970.300314","DOIUrl":null,"url":null,"abstract":"A classifier which, in general, implements a nonlinear decision boundary is shown to be equivalent to a linear discriminant function when the measurements are binary valued; its relation to the Bayes classifier is derived. The classifier requires less computation than a similar one based on the Euclidean distance and can perform equally well.","PeriodicalId":120916,"journal":{"name":"IEEE Trans. Syst. Sci. Cybern.","volume":"16 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1970-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"On a Simple Minkowski Metric Classifier\",\"authors\":\"G. Toussaint\",\"doi\":\"10.1109/TSSC.1970.300314\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A classifier which, in general, implements a nonlinear decision boundary is shown to be equivalent to a linear discriminant function when the measurements are binary valued; its relation to the Bayes classifier is derived. The classifier requires less computation than a similar one based on the Euclidean distance and can perform equally well.\",\"PeriodicalId\":120916,\"journal\":{\"name\":\"IEEE Trans. Syst. Sci. Cybern.\",\"volume\":\"16 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1970-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Trans. Syst. Sci. Cybern.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/TSSC.1970.300314\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Trans. Syst. Sci. Cybern.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TSSC.1970.300314","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A classifier which, in general, implements a nonlinear decision boundary is shown to be equivalent to a linear discriminant function when the measurements are binary valued; its relation to the Bayes classifier is derived. The classifier requires less computation than a similar one based on the Euclidean distance and can perform equally well.
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