{"title":"用于文本分类的信息向量机","authors":"M. Stankovic, S. Stankovic","doi":"10.1109/NEUREL.2006.341186","DOIUrl":null,"url":null,"abstract":"In this paper an analysis is given of the application of Bayesian Gaussian process statistical learning algorithms to the problem of text categorization. It is demonstrated that the informative vector machine method, as a sparse Bayesian compression scheme, provides results better than those obtained so far with the support vector machine method, with much less computational cost","PeriodicalId":231606,"journal":{"name":"2006 8th Seminar on Neural Network Applications in Electrical Engineering","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Informative Vector Machines for Text Categorization\",\"authors\":\"M. Stankovic, S. Stankovic\",\"doi\":\"10.1109/NEUREL.2006.341186\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper an analysis is given of the application of Bayesian Gaussian process statistical learning algorithms to the problem of text categorization. It is demonstrated that the informative vector machine method, as a sparse Bayesian compression scheme, provides results better than those obtained so far with the support vector machine method, with much less computational cost\",\"PeriodicalId\":231606,\"journal\":{\"name\":\"2006 8th Seminar on Neural Network Applications in Electrical Engineering\",\"volume\":\"22 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2006 8th Seminar on Neural Network Applications in Electrical Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NEUREL.2006.341186\",\"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 8th Seminar on Neural Network Applications in Electrical Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NEUREL.2006.341186","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Informative Vector Machines for Text Categorization
In this paper an analysis is given of the application of Bayesian Gaussian process statistical learning algorithms to the problem of text categorization. It is demonstrated that the informative vector machine method, as a sparse Bayesian compression scheme, provides results better than those obtained so far with the support vector machine method, with much less computational cost
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
