Pool-based sequential active learning with multi kernels

Jeongmin Chae; Songnam Hong

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arXiv · 2010.11421

Pool-based sequential active learning with multi kernels

Jeongmin Chae·Songnam Hong

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Abstract

We study a pool-based sequential active learning (AL), in which one sample is queried at each time from a large pool of unlabeled data according to a selection criterion. For this framework, we propose two selection criteria, named expected-kernel-discrepancy (EKD) and expected-kernel-loss (EKL), by leveraging the particular structure of multiple kernel learning (MKL). Also, it is identified that the proposed EKD and EKL successfully generalize the concepts of popular query-by-committee (QBC) and expected-model-change (EMC), respectively. Via experimental results with real-data sets, we verify the effectiveness of the proposed criteria compared with the existing methods.

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Jeongmin Chae, Songnam Hong. 2020-10-22. Pool-based sequential active learning with multi kernels. https://arxiv.org/abs/2010.11421

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