{"title":"基于分布映射指数的多变量数据分类","authors":"M. Jiřina","doi":"10.1109/ICCCYB.2006.305707","DOIUrl":null,"url":null,"abstract":"An exponent similar to the correlation dimension is introduced. This exponent is used for probability density estimation in high-dimensional spaces and for classification of multivariate data. It is also shown that this classifier exhibits significantly better behavior (classification accuracy) than other kinds of classifiers.","PeriodicalId":160588,"journal":{"name":"2006 IEEE International Conference on Computational Cybernetics","volume":"151 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Multivariate Data Classification Using the Distribution Mapping Exponent\",\"authors\":\"M. Jiřina\",\"doi\":\"10.1109/ICCCYB.2006.305707\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"An exponent similar to the correlation dimension is introduced. This exponent is used for probability density estimation in high-dimensional spaces and for classification of multivariate data. It is also shown that this classifier exhibits significantly better behavior (classification accuracy) than other kinds of classifiers.\",\"PeriodicalId\":160588,\"journal\":{\"name\":\"2006 IEEE International Conference on Computational Cybernetics\",\"volume\":\"151 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2006 IEEE International Conference on Computational Cybernetics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCCYB.2006.305707\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 IEEE International Conference on Computational Cybernetics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCCYB.2006.305707","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Multivariate Data Classification Using the Distribution Mapping Exponent
An exponent similar to the correlation dimension is introduced. This exponent is used for probability density estimation in high-dimensional spaces and for classification of multivariate data. It is also shown that this classifier exhibits significantly better behavior (classification accuracy) than other kinds of classifiers.
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