Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Analyzing the effectiveness and applicability of co-training
Proceedings of the ninth international conference on Information and knowledge management
Modular Learning in Neural Networks: A Modularized Approach to Neural Network Classification
Modular Learning in Neural Networks: A Modularized Approach to Neural Network Classification
Three learning phases for radial-basis-function networks
Neural Networks
Combining Labeled and Unlabeled Data for Text Classification with a Large Number of Categories
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Comparison of multiclass SVM decomposition schemes for visual object recognition
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
Novel weighting in single hidden layer feedforward neural networks for data classification
Computers & Mathematics with Applications
A novel hybrid neural learning algorithm using simulated annealing and quasisecant method
AusDM '11 Proceedings of the Ninth Australasian Data Mining Conference - Volume 121
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Supervised learning requires a large amount of labeled data but the data labeling process can be expensive and time consuming. Co-training is a semi-supervised learning method that reduces the amount of required labeled data through exploiting the available unlabeled data in supervised learning to boost the accuracy. It assumes that the patterns are described by multiple independent feature sets and each feature set is sufficient for classification. On the other hand, most of the real-world pattern recognition tasks involve a large number of categories which may make the task difficult. The tree-structured approach is a multi-class decomposition strategy where a complex multi-class problem is decomposed into tree structured binary sub-problems. In this paper, we propose a framework that combines the tree-structured approach with Co-training. We show that our framework is especially useful for classification tasks involving a large number of classes and a small amount of labeled data where the tree-structured approach does not perform well by itself and when combined with Co-training, the unlabeled data boosts its accuracy.