Transductive Text Classification

For many practical uses of text classification, it is crucial that the learner be able to generalize well using little training data. A news-filtering service, for example, requiring a hundred days’ worth of training data is unlikely to please even the most patient users. The work presented in the following tackles the problem of learning from small training samples by taking a transductive [Vapnik, 1998], instead of an inductive approach. In the inductive setting the learner tries to induce a decision function which has a low error rate on the whole distribution of examples for the particular learning task. Often, this setting is unnecessarily complex. In many situations we do not care about the particular decision function, but rather that we classify a given set of examples (i.e. a test set) with as few errors as possible. This is the goal of transductive inference.