Stay one forget multiple extreme learning machine with deep network using time interval process: A review

Agrata Shukla, Vijay Bhandari, Amit Shrivastava
{"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":null,"pages":null},"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}
引用次数: 2

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.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于时间间隔过程的深度网络多留一忘极端学习机综述
数据流是用于通信的数据包序列。试图预测的目标变量的属性在概念漂移发生时发生变化。所以,随着时间的推移,观测结果变得不那么准确了。当概念漂移的速度非常快时,以毫秒计,预测的准确性很难处理。为了解决这一问题,提出了一种基于时间间隔过程的深度网络多极限学习机。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Smart input: Provide mouse and keyboard input to a PC from android devices A hybrid approach for human skin detection Correlating multiple events and data in an ethernet network Data visualization through R and Azure for scaling machine training sets Robust machine learning of the complex-valued neurons
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1