Co-training is a well known semi-supervised learning algorithm, in which two classifiers are trained on two different views (feature sets): the initially small training set is iteratively updated with unlabelled samples classified with high confidence by one of the two classifiers. In this paper we address an issue that has been overlooked so far in the literature, namely, how co-training performance is affected by the size of the initial training set, as it decreases to the minimum value below which a given learning algorithm can not be applied anymore. In this paper we address this issue empirically, testing the algorithm on 24 real datasets artificially splitted in two views, using two different base classifiers. Our results show that a very small training set, even made up of one only labelled sample per class, does not adversely affect co-training performance.
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