Text Classification from Labeled and Unlabeled Documents using EM

被引：114
作者：
Kamal Nigam
Andrew Kachites Mccallum
Sebastian Thrun
Tom Mitchell
机构：
[1] Carnegie Mellon University,School of Computer Science
[2] Just Research,School of Computer Science
[3] Carnegie Mellon University,School of Computer Science
[4] Carnegie Mellon University,School of Computer Science
[5] Carnegie Mellon University,undefined
来源：
Machine Learning | 2000年 / 39卷
关键词：
text classification; Expectation-Maximization; integrating supervised and unsupervised learning; combining labeled and unlabeled data; Bayesian learning;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
This paper shows that the accuracy of learned text classifiers can be improved by augmenting a small number of labeled training documents with a large pool of unlabeled documents. This is important because in many text classification problems obtaining training labels is expensive, while large quantities of unlabeled documents are readily available.
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收藏
页码：103 / 134
页数：31
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