Selective Sampling for Classification

  • François Laviolette
  • Mario Marchand
  • Sara Shanian
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5032)

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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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • François Laviolette
    • 1
  • Mario Marchand
    • 1
  • Sara Shanian
    • 1
  1. 1.IFT-GLOUniversité LavalCanada

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