Using Unlabeled Data for Supervised Learning
Geoffrey G. Towell · Neural Information Processing Systems · 1995
Many classification problems have the property that the only costly part of obtaining examples is the class label. This paper suggests a simple method for using distribution information contained in unlabeled examples to augment labeled examples in a supervised training framework. Empirical tests show that the technique described in this paper can significantly improve the accuracy of a supervised learner when the learner is well below its asymptotic accuracy level.