{"title":"专家分布式学习模型的一致性","authors":"Joel B. Predd, S. Kulkarni, H. Poor","doi":"10.1109/ISIT.2004.1365502","DOIUrl":null,"url":null,"abstract":"Motivated by sensor networks and traditional methods of statistical pattern recognition, a model for distributed learning is formulated. The model is in line with learning models considered in the context of Stone-type classifiers, but differs in the dependency structure of the sampling process; questions of universal consistency are addressed.","PeriodicalId":269907,"journal":{"name":"International Symposium onInformation Theory, 2004. ISIT 2004. Proceedings.","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2004-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Consistency in a model for distributed learning with specialists\",\"authors\":\"Joel B. Predd, S. Kulkarni, H. Poor\",\"doi\":\"10.1109/ISIT.2004.1365502\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Motivated by sensor networks and traditional methods of statistical pattern recognition, a model for distributed learning is formulated. The model is in line with learning models considered in the context of Stone-type classifiers, but differs in the dependency structure of the sampling process; questions of universal consistency are addressed.\",\"PeriodicalId\":269907,\"journal\":{\"name\":\"International Symposium onInformation Theory, 2004. ISIT 2004. Proceedings.\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2004-10-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Symposium onInformation Theory, 2004. ISIT 2004. Proceedings.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISIT.2004.1365502\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Symposium onInformation Theory, 2004. ISIT 2004. Proceedings.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISIT.2004.1365502","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Consistency in a model for distributed learning with specialists
Motivated by sensor networks and traditional methods of statistical pattern recognition, a model for distributed learning is formulated. The model is in line with learning models considered in the context of Stone-type classifiers, but differs in the dependency structure of the sampling process; questions of universal consistency are addressed.