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USE OF UNLABELED DATA IN SUPERVISED MACHINE LEARNING

In many machine learning problem domains large amounts of data are available but the cost of correctly labeling it prohibits its use. This paper presents a short overview of methods for using a small set of labeled data together with a large supplementary unlabeled dataset in order to learn a better hypothesis than just by using the labeled information.