Using Unlabeled Data for Supervised Learning

Part of Advances in Neural Information Processing Systems 8 (NIPS 1995)

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Geoffrey Towell


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 de(cid:173) scribed in this paper can significantly improve the accuracy of a supervised learner when the learner is well below its asymptotic accuracy level.