Semi-supervised learning of tree-structured RBF networks using co-training

Abstract

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. © Springer-Verlag Berlin Heidelberg 2008.