Empirically Evaluating Co-Training
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2009-06-02
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en
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Abstract
Co-training is a classification scheme needing only a small set of training
instances for correct classification. The main question assessed in this thesis was how co-training performance varies with varying representativeness
of the training data. 1280 co-training runs have been made, to test the
generalization accuracy of co-training classification when using different
selections of the training data. The results indicate that the availability
of training data that are typical for their class or a distribution in the
training data matching the a priori distribution of the corpus as a whole
is a good condition for the generalization accuracy of co-training.
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Faculteit der Sociale Wetenschappen