.png){kind=icon}
返回
Learning from partially supervised data using mixture models and belief functions
.png){kind=icon}
OA
AI
DOI：10.1016/j.patcog.2008.07.014.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper addresses classification problems in which the class membership of training data are only partially known. Each learning sample is assumed to consist of a feature vector x(i) is an element of X and an imprecise and/or uncertain soft label m(i) defined as a Dempster-Shafer basic belief assignment over the set of classes. This framework thus generalizes many kinds of learning problems including supervised, unsupervised and semi-supervised learning. Here. it is assumed that the feature vectors are generated from a mixture model. Using the generalized Bayesian theorem, an extension of Bayes' theorem in the belief function framework, we derive a criterion generalizing the likelihood function. A variant of the expectation maximization (EM) algorithm, dedicated to the optimization of this criterion is proposed, allowing us to compute estimates of model parameters. Experimental results demonstrate the ability of this approach to exploit partial information about class labels. (C) 2008 Elsevier Ltd. All rights reserved.
Keyword:
Dempster-Shafer theory
Transferable belief model
Mixture models
EM algorithm
Classification
Clustering
Partially supervised learning
Semi-supervised learning
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
机构
引用论文
.png){kind=icon}
Coaches’ implicit associations between size and giftedness: implications for the relative age effect
PREAI
分享收藏
查看更多内容