Baseline Methods for Active Learning

Cawley, G ORCID: (2011) Baseline Methods for Active Learning. In: JMLR: Workshop and Conference Proceedings 16. JMLR Workshop and Conference Proceedings, 16 . Microtome, pp. 47-57.

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In many potential applications of machine learning, unlabelled data are abundantly available at low cost, but there is a paucity of labelled data, and labeling unlabelled examples is expensive and/or time-consuming. This motivates the development of active learning methods, that seek to direct the collection of labelled examples such that the greatest performance gains can be achieved using the smallest quantity of labelled data. In this paper, we describe some simple pool-based active learning strategies, based on optimally regularised linear [kernel] ridge regression, providing a set of baseline submissions for the Active Learning Challenge. A simple random strategy, where unlabelled patterns are submitted to the oracle purely at random, is found to be surprisingly e?ective, being competitive with more complex approaches.

Item Type: Book Section
Faculty \ School: Faculty of Science > School of Computing Sciences

UEA Research Groups: Faculty of Science > Research Groups > Computational Biology
Faculty of Science > Research Groups > Data Science and Statistics
Faculty of Science > Research Groups > Centre for Ocean and Atmospheric Sciences
Depositing User: Pure Connector
Date Deposited: 08 Jan 2016 10:01
Last Modified: 20 Jun 2023 14:51

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