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
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
CBC: Clustering Based Text Classification Requiring Minimal Labeled Data
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
An analysis of the relative hardness of Reuters-21578 subsets: Research Articles
Journal of the American Society for Information Science and Technology
Semi-supervised single-label text categorization using centroid-based classifiers
Proceedings of the 2007 ACM symposium on Applied computing
Detecting spam email by radial basis function networks
International Journal of Knowledge-based and Intelligent Engineering Systems
Pattern classification and clustering: A review of partially supervised learning approaches
Pattern Recognition Letters
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Semi-supervised text classification has numerous applications and is particularly applicable to the problems where large quantities of unlabeled data are readily available while only a small number of labeled training samples are accessible. The paper proposes a semi-supervised classifier that integrates a clustering based Expectation Maximization (EM) algorithm into radial basis function (RBF) neural networks and can learn for classification from a very small number of labeled training samples and a large pool of unlabeled data effectively. A generalized centroid clustering algorithm is also investigated in this work to balance predictive values between labeled and unlabeled training data and to improve classification accuracy. Experimental results with three popular text classification corpora show that the proper use of additional unlabeled data in this semi-supervised approach can reduce classification errors by up to 26%.