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There is a growing interest in exploring the use of unlabeled data as a way to improve classification performance in text categorization. The ready availability of this kind of data in most applications makes it an appealing source of information. This work reports a study carried out on the Reuters-21578 corpus to evaluate the performance of support vector machines when unlabeled examples are introduced in the learning process. The improvement achieved, especially in false negative values and therefore in recall values, demonstrates that the use of unlabeled examples can be very important for small data sets.