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论文类型：期刊论文

发表时间：2010-12-01

发表刊物：APPLIED INTELLIGENCE

收录刊物：SCIE、EI、Scopus

卷号：33

期号：3

页面范围：302-317

ISSN号：0924-669X

关键字：Semi-supervised learning; Bayesian ARTMAP; Expectation maximization; Classification; Incremental learning

摘要：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.