Enhancing Supervised Learning with Unlabeled Data
In a wide variety of supervised learning scenarios, there is a small set of labeled data, along with a large pool of unlabeled data. In this thesis, we present a new semi-supervised learning method called co-learning that is designed to use unlabeled data to enhance standard supervised learning algorithms. The idea is that two or more standard supervised learning algorithms can leverage off the fact that they have different representations of the hypotheses and they are likely to detect different patterns in labeled data. We also design an active co-learning strategy to bootstrap our co-leaning procedure when the originally labeled data set is too small to provide accurate confidence estimate for the learned hypotheses. We provide a priority sampling technique as the selection component in our active co-learning method. We evaluate our co-learning algorithms on several datasets from a commonly used data repository in the machine learning community. We also test our co-learning method on text categorization. The contribution of this research is to put forward a new semi-supervised learning approach for learning with a small number of labeled examples, and explore the applicability of our co-learning strategy in real world applications.