On the exponential value of labeled samples
Pattern Recognition Letters
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Analyzing the effectiveness and applicability of co-training
Proceedings of the ninth international conference on Information and knowledge management
Machine Learning
Enhancing Supervised Learning with Unlabeled Data
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Positive and Unlabeled Examples Help Learning
ALT '99 Proceedings of the 10th International Conference on Algorithmic Learning Theory
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Active learning with statistical models
Journal of Artificial Intelligence Research
Active learning with committees for text categorization
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Estimating NBC-based recommendations on arbitrarily partitioned data with privacy
Knowledge-Based Systems
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In many applications, an enormous amount of unlabeled data is available with little cost. Therefore, it is natural to ask whether we can take advantage of these unlabeled data in classification learning. In this paper, we analyzed the role of unlabeled data in the context of naive Bayesian learning. Experimental results show that including unlabeled data as part of training data can significantly improve the performance of classification accuracy.