{"title":"一种基于多个成对分类器的多类分类方法","authors":"Tomoyuki Hamamura, H. Mizutani, Bunpei Irie","doi":"10.1109/ICDAR.2003.1227774","DOIUrl":null,"url":null,"abstract":"In this paper, a new method of composing a multi-classclassifier using pairwise classifiers is proposed. A\"Resemblance Model\" is exploited to calculate aposteriori probability for combining pairwise classifiers.We proved the validity of this model by usingapproximation of a posteriori probability formula. Usingthis theory, we can obtain the optimal decision. Anexperimental result of handwritten numeral recognition ispresented, supporting the effectiveness of our method.","PeriodicalId":249193,"journal":{"name":"Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings.","volume":"217 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2003-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"25","resultStr":"{\"title\":\"A multiclass classification method based on multiple pairwise classifiers\",\"authors\":\"Tomoyuki Hamamura, H. Mizutani, Bunpei Irie\",\"doi\":\"10.1109/ICDAR.2003.1227774\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, a new method of composing a multi-classclassifier using pairwise classifiers is proposed. A\\\"Resemblance Model\\\" is exploited to calculate aposteriori probability for combining pairwise classifiers.We proved the validity of this model by usingapproximation of a posteriori probability formula. Usingthis theory, we can obtain the optimal decision. Anexperimental result of handwritten numeral recognition ispresented, supporting the effectiveness of our method.\",\"PeriodicalId\":249193,\"journal\":{\"name\":\"Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings.\",\"volume\":\"217 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2003-08-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"25\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICDAR.2003.1227774\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDAR.2003.1227774","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A multiclass classification method based on multiple pairwise classifiers
In this paper, a new method of composing a multi-classclassifier using pairwise classifiers is proposed. A"Resemblance Model" is exploited to calculate aposteriori probability for combining pairwise classifiers.We proved the validity of this model by usingapproximation of a posteriori probability formula. Usingthis theory, we can obtain the optimal decision. Anexperimental result of handwritten numeral recognition ispresented, supporting the effectiveness of our method.
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