A Probabilistic Model-Free Approach in Learning Multivariate Noisy Linear Systems

L. State, Iuliana Paraschiv-Munteanu
{"title":"A Probabilistic Model-Free Approach in Learning Multivariate Noisy Linear Systems","authors":"L. State, Iuliana Paraschiv-Munteanu","doi":"10.1109/SYNASC.2011.14","DOIUrl":null,"url":null,"abstract":"The paper provides a series of results concerning the learning from data a linear regressive model in a multivariate framework. The parameter estimates of the regressive model are determined using the maximum likelihood principle and the adaptive learning algorithms are derived using the gradient ascent technique. The predicted output is expressed as the sum of a linear combination of the entries of the input and the random vector that represents the effects of the unobservable factors and noise. In the second section of the paper the mathematical arguments for the estimation scheme based exclusively on a finite size set of observations is provided. The third section of the paper is focused on experimental evaluation of the quality of the resulted learning scheme in order to establish conclusions concerning their accuracy and generalization capacities, the evaluation being performed in terms of metric, probabilistic and informational criterion functions. The final section of the paper contains a series of conclusions and suggestions for further work.","PeriodicalId":184344,"journal":{"name":"2011 13th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing","volume":"68 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 13th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SYNASC.2011.14","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

The paper provides a series of results concerning the learning from data a linear regressive model in a multivariate framework. The parameter estimates of the regressive model are determined using the maximum likelihood principle and the adaptive learning algorithms are derived using the gradient ascent technique. The predicted output is expressed as the sum of a linear combination of the entries of the input and the random vector that represents the effects of the unobservable factors and noise. In the second section of the paper the mathematical arguments for the estimation scheme based exclusively on a finite size set of observations is provided. The third section of the paper is focused on experimental evaluation of the quality of the resulted learning scheme in order to establish conclusions concerning their accuracy and generalization capacities, the evaluation being performed in terms of metric, probabilistic and informational criterion functions. The final section of the paper contains a series of conclusions and suggestions for further work.
一种无概率模型的多元噪声线性系统学习方法
本文给出了多变量框架下线性回归模型数据学习的一系列结果。利用极大似然原理确定了回归模型的参数估计,并利用梯度上升技术推导了自适应学习算法。预测的输出表示为输入项和表示不可观察因素和噪声影响的随机向量的线性组合的总和。在论文的第二部分,给出了仅基于有限大小的观测集的估计方案的数学论据。本文的第三部分侧重于对所得到的学习方案的质量进行实验评估,以建立关于其准确性和泛化能力的结论,评估是根据度量、概率和信息准则函数进行的。论文的最后一部分包含了一系列结论和对进一步工作的建议。
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 求助全文
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