Query Learning with Large Margin Classifiers
The active selection of instances can significantly improve the generalisation performance of a learning machine. Large margin classifiers such as support vector machines classify data using the most informative instances (the support vectors). This makes them natural candidates for instance selection strategies. In this paper we propose an algorithm for the training of support vector machines using instance selection. We give a theoretical justification for the strategy and experimental results on real and artificial data demonstrating its effectiveness. The technique is most efficient when the data set can be learnt using few support vectors.