Efficient active learning of halfspaces: An aggressive approach

Alon Gonen, Sivan Sabato, Shai Shalev-Shwartz

Research output: Contribution to journalArticlepeer-review

21 Scopus citations

Abstract

We study pool-based active learning of half-spaces. We revisit the aggressive approach for active learning in the realizable case, and show that it can be made efficient and practical, while also having theoretical guarantees under reasonable assumptions. We further show, both theoretically and experimentally, that it can be preferable to mellow approaches. Our efficient aggressive active learner of half-spaces has formal approximation guarantees that hold when the pool is separable with a margin. While our analysis is focused on the realizable setting, we show that a simple heuristic allows using the same algorithm successfully for pools with low error as well. We further compare the aggressive approach to the mellow approach, and prove that there are cases in which the aggressive approach results in significantly better label complexity compared to the mellow approach. We demonstrate experimentally that substantial improvements in label complexity can be achieved using the aggressive approach, for both realizable and low-error settings.

Original languageEnglish
Pages (from-to)2513-2615
Number of pages103
JournalJournal of Machine Learning Research
Volume14
StatePublished - 1 Sep 2013
Externally publishedYes

Keywords

  • Active learning
  • Adaptive sub-modularity
  • Linear classifiers
  • Margin

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Statistics and Probability
  • Artificial Intelligence

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