Empirically Evaluating Co-Training

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dc.contributor.advisorSprinkhuizen-Kuyper, I.G.
dc.contributor.advisorVuurpijl, L.G.
dc.contributor.authorBeusekom, W.E. van
dc.date.issued2009-06-02
dc.description.abstractCo-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.en_US
dc.identifier.urihttp://theses.ubn.ru.nl/handle/123456789/55
dc.language.isoenen_US
dc.thesis.facultyFaculteit der Sociale Wetenschappenen_US
dc.thesis.specialisationBachelor Artificial Intelligenceen_US
dc.thesis.studyprogrammeArtificial Intelligenceen_US
dc.thesis.typeBacheloren_US
dc.titleEmpirically Evaluating Co-Trainingen_US
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