On the Scarcity of Labeled Data

Abstract

Scarcity of labeled data can be encountered in various engineering applications due to several factors. This raises the question of how to generate sufficient amounts of labeled data when it is sparse in order to build effective learning tools. One approach to overcome this problem is to use unlabeled data. In this paper, we propose two approaches, each is… (More)
DOI: 10.1109/CIMCA.2005.1631299

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