基于矢量量化的非参数分类器设计

Proceedings of 1994 Workshop on Information Theory and Statistics Pub Date : 1994-10-27 DOI:10.1109/WITS.1994.513862

Q. Xie, R. Ward, C. Laszlo

{"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}

引用次数: 1

摘要

基于vq的方法是一种有效的非参数分类器设计数据约简技术。这种新技术在坚持具有竞争力的分类精度的同时，克服了传统非参数分类器计算复杂和需要大量计算机存储的缺点。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

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.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Proceedings of 1994 Workshop on Information Theory and Statistics

自引率

0.00%

发文量