多传感器自适应处理的协同训练算法

Joel B. Predd, Sanjeev R. Kulkarni, H. V. Poor
{"title":"多传感器自适应处理的协同训练算法","authors":"Joel B. Predd, Sanjeev R. Kulkarni, H. V. Poor","doi":"10.1109/CAMSAP.2007.4498024","DOIUrl":null,"url":null,"abstract":"In this paper, we discuss a local message passing algorithm for collaboratively training networks of kernel-linear least-squares regression estimators. The algorithm is constructed to solve a relaxation of the classical centralized kernel- linear least-squares regression problem. A statistical analysis shows that the generalization error afforded agents by the collaborative training algorithm can be bounded in terms of the relationship between the network topology and the representational capacity of the relevant reproducing kernel Hilbert space; this is in contrast to related approaches which relate the similarity structure encoded in the kernel and the network topology. The algorithm is relevant to the problem of distributed learning in wireless sensor networks by virtue of its exploitation of local communication.","PeriodicalId":220687,"journal":{"name":"2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing","volume":"12 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"A Collaborative Training Algorithm for Multi-Sensor Adaptive Processing\",\"authors\":\"Joel B. Predd, Sanjeev R. Kulkarni, H. V. Poor\",\"doi\":\"10.1109/CAMSAP.2007.4498024\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we discuss a local message passing algorithm for collaboratively training networks of kernel-linear least-squares regression estimators. The algorithm is constructed to solve a relaxation of the classical centralized kernel- linear least-squares regression problem. A statistical analysis shows that the generalization error afforded agents by the collaborative training algorithm can be bounded in terms of the relationship between the network topology and the representational capacity of the relevant reproducing kernel Hilbert space; this is in contrast to related approaches which relate the similarity structure encoded in the kernel and the network topology. The algorithm is relevant to the problem of distributed learning in wireless sensor networks by virtue of its exploitation of local communication.\",\"PeriodicalId\":220687,\"journal\":{\"name\":\"2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing\",\"volume\":\"12 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CAMSAP.2007.4498024\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CAMSAP.2007.4498024","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

摘要

本文讨论了核线性最小二乘回归估计协同训练网络的局部消息传递算法。该算法的构造是为了解决经典的集中式核线性最小二乘回归问题的一个松弛。统计分析表明,协同训练算法给智能体提供的泛化误差可以根据网络拓扑与相关再现核希尔伯特空间的表示能力之间的关系来限定;这与将核中编码的相似结构与网络拓扑相关联的相关方法形成对比。该算法利用了局部通信的特点,解决了无线传感器网络中的分布式学习问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Collaborative Training Algorithm for Multi-Sensor Adaptive Processing
In this paper, we discuss a local message passing algorithm for collaboratively training networks of kernel-linear least-squares regression estimators. The algorithm is constructed to solve a relaxation of the classical centralized kernel- linear least-squares regression problem. A statistical analysis shows that the generalization error afforded agents by the collaborative training algorithm can be bounded in terms of the relationship between the network topology and the representational capacity of the relevant reproducing kernel Hilbert space; this is in contrast to related approaches which relate the similarity structure encoded in the kernel and the network topology. The algorithm is relevant to the problem of distributed learning in wireless sensor networks by virtue of its exploitation of local communication.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信