On the exponential value of labeled samples
Pattern Recognition Letters
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
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
Enhancing Supervised Learning with Unlabeled Data
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
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
Multinomial mixture model with feature selection for text clustering
Knowledge-Based Systems
Scenario analysis using Bayesian networks: A case study in energy sector
Knowledge-Based Systems
Semi-supervised learning based on nearest neighbor rule and cut edges
Knowledge-Based Systems
A 'non-parametric' version of the naive Bayes classifier
Knowledge-Based Systems
Improving the performance of association classifiers by rule prioritization
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.