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Learning a generative classifier from label proportions
Fan, Kai1; Zhang, Hongyi1; Yan, Songbai1; Wang, Liwei1; Zhang, Wensheng2; Feng, Jufu1
Source PublicationNEUROCOMPUTING
2014-09-02
Volume139Pages:47-55
SubtypeArticle
AbstractLearning a classifier when only knowing the features and marginal distribution of class labels in each of the data groups is both theoretically interesting and practically useful. Specifically, we consider the case in which the ratio of the number of data instances to the number of classes is large. We prove sample complexity upper bound in this setting, which is inspired by an analysis of existing algorithms. We further formulate the problem in a density estimation framework to learn a generative classifier. We also develop a practical RBM-based algorithm which shows promising performance on benchmark datasets. (C) 2014 Elsevier B.V. All rights reserved.
KeywordProportion Learning Bayesian Model Restricted Boltzmann Machine
WOS HeadingsScience & Technology ; Technology
Indexed BySCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000337661800006
Citation statistics
Cited Times:7[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/10748
Collection精密感知与控制研究中心_精密感知与控制
Affiliation1.Peking Univ, Sch Elect Engn & Comp Sci, MOE, Key Lab Machine Percept, Beijing, Peoples R China
2.Chinese Acad Sci, Inst Automat, Beijing 100864, Peoples R China
Recommended Citation
GB/T 7714
Fan, Kai,Zhang, Hongyi,Yan, Songbai,et al. Learning a generative classifier from label proportions[J]. NEUROCOMPUTING,2014,139:47-55.
APA Fan, Kai,Zhang, Hongyi,Yan, Songbai,Wang, Liwei,Zhang, Wensheng,&Feng, Jufu.(2014).Learning a generative classifier from label proportions.NEUROCOMPUTING,139,47-55.
MLA Fan, Kai,et al."Learning a generative classifier from label proportions".NEUROCOMPUTING 139(2014):47-55.
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