Abstract
Supervised learning is concerned with the task of building accurate classifiers from a set of labelled examples. However, the task of gathering a large set of labelled examples can be costly and time-consuming. Active learning algorithms try to reduce this labelling cost by performing a small number of label-queries from a large set of unlabelled examples during the process of building a classifier. However, the level of performance achieved by active learning algorithms is not always up to our expectations and no rigorous performance guarantee, in the form of a risk bound, exists for non-trivial active learning algorithms. In this paper, we propose a novel (and easy to implement) active learning algorithm having a rigorous performance guarantee (i.e., a valid risk bound) and that performs very well in comparison with some widely-used active learning algorithms.
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Laviolette, F., Marchand, M., Shanian, S. (2008). Selective Sampling for Classification. In: Bergler, S. (eds) Advances in Artificial Intelligence. Canadian AI 2008. Lecture Notes in Computer Science(), vol 5032. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68825-9_19
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DOI: https://doi.org/10.1007/978-3-540-68825-9_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68821-1
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