{"title":"基于时间间隔过程的深度网络多留一忘极端学习机综述","authors":"Agrata Shukla, Vijay Bhandari, Amit Shrivastava","doi":"10.1109/CSNT.2017.8418548","DOIUrl":null,"url":null,"abstract":"Data streams are the sequence of data packets for communication. The properties of the target variable that is trying to predict, changes at the occurrence of concept drift. So, The observations become less accurate as the time passes. When the speed of concept drift is very fast, In terms of milliseconds, the accuracy of predictions is very difficult to handle. So to solve the problem the new stay one forget multiple extreme learning machine with deep network using time interval process is proposed.","PeriodicalId":382417,"journal":{"name":"2017 7th International Conference on Communication Systems and Network Technologies (CSNT)","volume":"116 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Stay one forget multiple extreme learning machine with deep network using time interval process: A review\",\"authors\":\"Agrata Shukla, Vijay Bhandari, Amit Shrivastava\",\"doi\":\"10.1109/CSNT.2017.8418548\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Data streams are the sequence of data packets for communication. The properties of the target variable that is trying to predict, changes at the occurrence of concept drift. So, The observations become less accurate as the time passes. When the speed of concept drift is very fast, In terms of milliseconds, the accuracy of predictions is very difficult to handle. So to solve the problem the new stay one forget multiple extreme learning machine with deep network using time interval process is proposed.\",\"PeriodicalId\":382417,\"journal\":{\"name\":\"2017 7th International Conference on Communication Systems and Network Technologies (CSNT)\",\"volume\":\"116 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 7th International Conference on Communication Systems and Network Technologies (CSNT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CSNT.2017.8418548\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 7th International Conference on Communication Systems and Network Technologies (CSNT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CSNT.2017.8418548","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Stay one forget multiple extreme learning machine with deep network using time interval process: A review
Data streams are the sequence of data packets for communication. The properties of the target variable that is trying to predict, changes at the occurrence of concept drift. So, The observations become less accurate as the time passes. When the speed of concept drift is very fast, In terms of milliseconds, the accuracy of predictions is very difficult to handle. So to solve the problem the new stay one forget multiple extreme learning machine with deep network using time interval process is proposed.