Abstract

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 dataset can be learnt using few support vectors. 1. Introduction The labour-intensive task of labelling data is a serious bottleneck for many data mining tasks. Often cost or time constraints mean that only a fraction of the available instances can be labeled. For this reason there has been increasing interest in the problem of handling partially labeled datasets. One approach ...

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