CASIA OpenIR  > 模式识别国家重点实验室  > 多媒体计算与图形学
Parameter Identifiability in Statistical Machine Learning: A Review
Ran, Zhi-Yong1; Hu, Bao-Gang2
Source PublicationNEURAL COMPUTATION
2017-05-01
Volume29Issue:5Pages:1151-1203
SubtypeReview
AbstractThis review examines the relevance of parameter identifiability for statistical models used in machine learning. In addition to defining main concepts, we address several issues of identifiability closely related to machine learning, showing the advantages and disadvantages of state-of- the-art research and demonstrating recent progress. First, we review criteria for determining the parameter structure of models from the literature. This has three related issues: parameter identifiability, parameter redundancy, and reparameterization. Second, we review the deep influence of identifiability on various aspects of machine learning from theoretical and application viewpoints. In addition to illustrating the utility and influence of identifiability, we emphasize the interplay among identifiability theory, machine learning, mathematical statistics, information theory, optimization theory, information geometry, Riemann geometry, symbolic computation, Bayesian inference, algebraic geometry, and others. Finally, we present a new perspective together with the associated challenges.
KeywordParameter Identifiability Statistical Machine Learning
WOS HeadingsScience & Technology ; Technology ; Life Sciences & Biomedicine
DOI10.1162/NECO_a_00947
WOS KeywordNATURAL GRADIENT DESCENT ; MULTILAYER NEURAL-NETWORKS ; SOFT COMMITTEE MACHINES ; INFORMATION CRITERION ; STRUCTURAL IDENTIFIABILITY ; COMPARTMENTAL-MODELS ; COMPUTER ALGEBRA ; LIKELIHOOD RATIO ; HIDDEN UNITS ; SINGULARITIES
Indexed BySCI ; SSCi
Language英语
Funding OrganizationNSFC(61273196 ; 61620106003)
WOS Research AreaComputer Science ; Neurosciences & Neurology
WOS SubjectComputer Science, Artificial Intelligence ; Neurosciences
WOS IDWOS:000399679500001
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/15089
Collection模式识别国家重点实验室_多媒体计算与图形学
Affiliation1.Chongqing Univ Posts & Telecommun, Chongqing Key Lab Computat Intelligence, Sch Comp Sci & Technol, Chongqing 400065, Peoples R China
2.Chinese Acad Sci, Inst Automat, NLPR & LIAMA, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Ran, Zhi-Yong,Hu, Bao-Gang. Parameter Identifiability in Statistical Machine Learning: A Review[J]. NEURAL COMPUTATION,2017,29(5):1151-1203.
APA Ran, Zhi-Yong,&Hu, Bao-Gang.(2017).Parameter Identifiability in Statistical Machine Learning: A Review.NEURAL COMPUTATION,29(5),1151-1203.
MLA Ran, Zhi-Yong,et al."Parameter Identifiability in Statistical Machine Learning: A Review".NEURAL COMPUTATION 29.5(2017):1151-1203.
Files in This Item: Download All
File Name/Size DocType Version Access License
Ran17.pdf(466KB)期刊论文作者接受稿开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Ran, Zhi-Yong]'s Articles
[Hu, Bao-Gang]'s Articles
Baidu academic
Similar articles in Baidu academic
[Ran, Zhi-Yong]'s Articles
[Hu, Bao-Gang]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Ran, Zhi-Yong]'s Articles
[Hu, Bao-Gang]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: Ran17.pdf
Format: Adobe PDF
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.