Active Learning of Halfspaces under a Margin Assumption

Alon Gonen, Sivan Sabato, Shai Shalev-Shwartz

Research output: Working paper/PreprintPreprint

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Abstract

We derive and analyze a new, efficient, pool-based active learning algorithm for halfspaces, called ALuMA. Most previous algorithms show exponential improvement in the label complexity assuming that the distribution over the instance space is close to uniform. This assumption rarely holds in practical applications. Instead, we study the label complexity under a large-margin assumption -- a much more realistic condition, as evident by the success of margin-based algorithms such as SVM. Our algorithm is computationally efficient and comes with formal guarantees on its label complexity. It also naturally extends to the non-separable case and to non-linear kernels. Experiments illustrate the clear advantage of ALuMA over other active learning algorithms.
Original languageEnglish
StatePublished - 2011

Publication series

NamearXiv:1112.1556 [cs.LG]

Keywords

  • Computer Science - Machine Learning
  • Statistics - Machine Learning

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