Online Choice of Active Learning Algorithms

  • Baram Y
  • El-Yaniv R
  • Luz K
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This work is concerned with the question of how to combine online an ensemble of active learners so as to expedite the learning progress in pool-based active learning. We develop an active-learning master algorithm, based on a known competitive algorithm for the multi-armed bandit problem. A major challenge in successfully choosing top performing active learners online is to reliably estimate their progress during the learning session. To this end we propose a simple maximum entropy criterion that provides effective estimates in realistic settings. We study the performance of the proposed master algorithm using an ensemble containing two of the best known active-learning algorithms as well as a new algorithm. The resulting active-learning master algorithm is empirically shown to consistently perform almost as well as and sometimes outperform the best algorithm in the ensemble on a range of classification problems.

Author-supplied keywords

  • Computational
  • Information-Theoretic Learning with Statistics
  • Theory & Algorithms

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  • Yoram Baram

  • Ran El-Yaniv

  • Kobi Luz

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