Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Exploiting generative models in discriminative classifiers
Proceedings of the 1998 conference on Advances in neural information processing systems II
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 in automated text categorization
ACM Computing Surveys (CSUR)
Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Partially Supervised Classification of Text Documents
ICML '02 Proceedings of the Nineteenth 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
Building Text Classifiers Using Positive and Unlabeled Examples
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Using urls and table layout for web classification tasks
Proceedings of the 13th international conference on World Wide Web
Improvements to Platt's SMO Algorithm for SVM Classifier Design
Neural Computation
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Since 1998 there has been significant interest in supervised learning algorithms that combine labeled and unlabeled data for text learning tasks. The co-training algorithm applied to datasets which have a natural separation of their features into two disjoint sets. In this paper, we demonstrate that when learning from labeled and unlabeled data using co-training algorithm, selecting those document examples first which have two parts of best matching features can obtain a good performance.