Using Unlabeled Data for Supervised Learning
نویسنده
چکیده
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.
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تاریخ انتشار 1995