USE OF UNLABELED DATA IN SUPERVISED MACHINE LEARNING
Blaz Novak
In: SIKDD 2004 at multiconference IS 2004, 12-15 Oct 2004, Ljubljana, Slovenia.

Abstract

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.

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