{"title":"基于矢量量化的非参数分类器设计","authors":"Q. Xie, R. Ward, C. Laszlo","doi":"10.1109/WITS.1994.513862","DOIUrl":null,"url":null,"abstract":"VQ-based method is developed as an effective data reduction technique for nonparametric classifier design. This new technique, while insisting on competitive classification accuracy, is found to overcome the usual disadvantage of traditional nonparametric classifiers of being computationally complex and of requiring large amounts of computer storage.","PeriodicalId":423518,"journal":{"name":"Proceedings of 1994 Workshop on Information Theory and Statistics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1994-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Nonparametric classifier design using vector quantization\",\"authors\":\"Q. Xie, R. Ward, C. Laszlo\",\"doi\":\"10.1109/WITS.1994.513862\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"VQ-based method is developed as an effective data reduction technique for nonparametric classifier design. This new technique, while insisting on competitive classification accuracy, is found to overcome the usual disadvantage of traditional nonparametric classifiers of being computationally complex and of requiring large amounts of computer storage.\",\"PeriodicalId\":423518,\"journal\":{\"name\":\"Proceedings of 1994 Workshop on Information Theory and Statistics\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1994-10-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of 1994 Workshop on Information Theory and Statistics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/WITS.1994.513862\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of 1994 Workshop on Information Theory and Statistics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WITS.1994.513862","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Nonparametric classifier design using vector quantization
VQ-based method is developed as an effective data reduction technique for nonparametric classifier design. This new technique, while insisting on competitive classification accuracy, is found to overcome the usual disadvantage of traditional nonparametric classifiers of being computationally complex and of requiring large amounts of computer storage.
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