Pool-based sequential active learning with multi kernels

2020/10/22 by Jeongmin Chae, Songnam Hong, Chae, Jeongmin +2
Computer Science · #Algorithms and Data Compression #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning and Algorithms #Machine Learning and Data Classification #cs.LG

paper · pdf · doi:10.48550/arxiv.2010.11421

arxiv created 2020/10/22 · openalex publication_date 2020/10/22 · arxiv updated 2020/10/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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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