The effect of unlabeled data on generative classifiers, with application

Cohen, Ira ; Cozman, Fabio G. ; Bronstein, Alexandre

HP Development Company

关键词: semi-supervised learning;    labeled and unlabeled data problem;    classification;    machine learning;   

RP-ID  :  HPL-2002-140

学科分类：计算机科学（综合）

美国|英语

来源: HP Labs

【 摘 要 】

In this paper we investigate the effect of unlabeled data on generative classifiers in semi-supervised learning. We first characterize situations where unlabeled data cannot change estimates obtained with labeled data, and argue that such situations are unusual in practice. We then report on a large set of experiments involving labeled and unlabeled data, and demonstrate that unlabeled data can degrade classification performance when modeling assumptions are incorrect. To improve classification performance, we propose a method to switch assumed model structure based on the effect of unlabeled data. 16 Pages