Learning a generative classifier from label proportions
Fan, Kai1; Zhang, Hongyi1; Yan, Songbai1; Wang, Liwei1; Zhang, Wensheng2; Feng, Jufu1
2014-09-02
发表期刊NEUROCOMPUTING
卷号139页码:47-55
文章类型Article
摘要Learning 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.
关键词Proportion Learning Bayesian Model Restricted Boltzmann Machine
WOS标题词Science & Technology ; Technology
收录类别SCI
语种英语
WOS研究方向Computer Science
WOS类目Computer Science, Artificial Intelligence
WOS记录号WOS:000337661800006
引用统计
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/10748
专题精密感知与控制研究中心_精密感知与控制
作者单位1.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
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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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