{"title":"传感器网络分类算法的大规模分析","authors":"F. Fagnani, S. Fosson, C. Ravazzi","doi":"10.1109/CDC.2012.6425917","DOIUrl":null,"url":null,"abstract":"This paper is devoted to study an iterative estimation/classification algorithm over a sensor network with faulty units recently appeared in the literature. We here present a complete analysis of the performance of the algorithm when the number of units goes to infinity both in terms of estimation and of classification error. In particular it is shown that the algorithm solution converges to the optimal Maximum Likelihood estimator.","PeriodicalId":312426,"journal":{"name":"2012 IEEE 51st IEEE Conference on Decision and Control (CDC)","volume":"93 6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A large scale analysis of a classification algorithm over sensor networks\",\"authors\":\"F. Fagnani, S. Fosson, C. Ravazzi\",\"doi\":\"10.1109/CDC.2012.6425917\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper is devoted to study an iterative estimation/classification algorithm over a sensor network with faulty units recently appeared in the literature. We here present a complete analysis of the performance of the algorithm when the number of units goes to infinity both in terms of estimation and of classification error. In particular it is shown that the algorithm solution converges to the optimal Maximum Likelihood estimator.\",\"PeriodicalId\":312426,\"journal\":{\"name\":\"2012 IEEE 51st IEEE Conference on Decision and Control (CDC)\",\"volume\":\"93 6 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 IEEE 51st IEEE Conference on Decision and Control (CDC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CDC.2012.6425917\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE 51st IEEE Conference on Decision and Control (CDC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CDC.2012.6425917","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A large scale analysis of a classification algorithm over sensor networks
This paper is devoted to study an iterative estimation/classification algorithm over a sensor network with faulty units recently appeared in the literature. We here present a complete analysis of the performance of the algorithm when the number of units goes to infinity both in terms of estimation and of classification error. In particular it is shown that the algorithm solution converges to the optimal Maximum Likelihood estimator.