期刊
APPLIED INTELLIGENCE
卷 33, 期 3, 页码 302-317出版社
SPRINGER
DOI: 10.1007/s10489-009-0167-x
关键词
Semi-supervised learning; Bayesian ARTMAP; Expectation maximization; Classification; Incremental learning
资金
- National Nature Science Foundation of China [60674073]
- National Major Technology R&D Program of China [2006BAB14B05]
- National Basic Research Program of China (973 Program) [2006CB403405]
This paper proposes a semi-supervised Bayesian ARTMAP (SSBA) which integrates the advantages of both Bayesian ARTMAP (BA) and Expectation Maximization (EM) algorithm. SSBA adopts the training framework of BA, which makes SSBA adaptively generate categories to represent the distribution of both labeled and unlabeled training samples without any user's intervention. In addition, SSBA employs EM algorithm to adjust its parameters, which realizes the soft assignment of training samples to categories instead of the hard assignment such as winner takes all. Experimental results on benchmark and real world data sets indicate that the proposed SSBA achieves significantly improved performance compared with BA and EM-based semi-supervised learning method; SSBA is appropriate for semi-supervised classification tasks with large amount of unlabeled samples or with strict demands for classification accuracy.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据