Selective Sampling Using the Query by Committee Algorithm
Machine Learning
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Analyzing the effectiveness and applicability of co-training
Proceedings of the ninth international conference on Information and knowledge management
Active + Semi-supervised Learning = Robust Multi-View Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Selective Sampling with Redundant Views
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Employing EM and Pool-Based Active Learning for Text Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Semi-supervised learning by disagreement
Knowledge and Information Systems
Improve Computer-Aided Diagnosis With Machine Learning Techniques Using Undiagnosed Samples
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Training pool selection for semi-supervised learning
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part I
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Semi-supervised learning reduces the cost of labeling the training data of a supervised learning algorithm through using unlabeled data together with labeled data to improve the performance. Co-Training is a popular semi-supervised learning algorithm, that requires multiple redundant and independent sets of features (views). In many real-world application domains, this requirement can not be satisfied. In this paper, a single-view variant of Co-Training, CoBC (Co-Training by Committee), is proposed, which requires an ensemble of diverse classifiers instead of the redundant and independent views. Then we introduce two new learning algorithms, QBC-then-CoBC and QBC-with-CoBC, which combines the merits of committee-based semi-supervised learning and committee-based active learning. An empirical study on handwritten digit recognition is conducted where the random subspace method (RSM) is used to create ensembles of diverse C4.5 decision trees. Experiments show that these two combinations outperform the other non committee-based ones.