{"title":"分析了MSVMO方法-支持向量机的概率输出","authors":"A. Madevska-Bogdanova, Z. Popeska, D. Nikolik","doi":"10.1109/ITI.2005.1491157","DOIUrl":null,"url":null,"abstract":"Number of experiments has shown that for classification problems, if SVM models trained over the same data set have similar SVM outputs with respect to a given input vector x, the values of the corresponding MSVMO probabilities [3] have small differences. This means that the information from the MSVMO method from different SVM models trained over the same data set is essentially the same.","PeriodicalId":392003,"journal":{"name":"27th International Conference on Information Technology Interfaces, 2005.","volume":"100 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2005-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Analysis of the MSVMO method - the probabilistic SVM outputs\",\"authors\":\"A. Madevska-Bogdanova, Z. Popeska, D. Nikolik\",\"doi\":\"10.1109/ITI.2005.1491157\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Number of experiments has shown that for classification problems, if SVM models trained over the same data set have similar SVM outputs with respect to a given input vector x, the values of the corresponding MSVMO probabilities [3] have small differences. This means that the information from the MSVMO method from different SVM models trained over the same data set is essentially the same.\",\"PeriodicalId\":392003,\"journal\":{\"name\":\"27th International Conference on Information Technology Interfaces, 2005.\",\"volume\":\"100 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2005-06-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"27th International Conference on Information Technology Interfaces, 2005.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ITI.2005.1491157\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"27th International Conference on Information Technology Interfaces, 2005.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ITI.2005.1491157","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Analysis of the MSVMO method - the probabilistic SVM outputs
Number of experiments has shown that for classification problems, if SVM models trained over the same data set have similar SVM outputs with respect to a given input vector x, the values of the corresponding MSVMO probabilities [3] have small differences. This means that the information from the MSVMO method from different SVM models trained over the same data set is essentially the same.
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