In many topic identification applications, supervised training labels are indirectly related to the semantic content of the documents being classified. For example, many topical...
Abstract. In many supervised learning tasks it can be costly or infeasible to obtain objective, reliable labels. We may, however, be able to obtain a large number of subjective, po...
Hierarchical categorization of documents is a task receiving growing interest due to the widespread proliferation of topic hierarchies for text documents. The worst problem of hie...
Recently there has been significant interest in supervised learning algorithms that combine labeled and unlabeled data for text learning tasks. The co-training setting [1] applie...
For many supervised learning tasks it is very costly to produce training data with class labels. Active learning acquires data incrementally, at each stage using the model learned...