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Cawley, G 
ORCID: https://orcid.org/0000-0002-4118-9095
(2011)
Baseline Methods for Active Learning.
In:
JMLR: Workshop and Conference Proceedings 16.
JMLR Workshop and Conference Proceedings, 16
.
Microtome, pp. 47-57.
Abstract
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 University of East Anglia > Faculty of Science > Research Groups > Computational Biology (subgroups are shown below) > Machine learning in computational biology |
| Depositing User: | Pure Connector |
| Date Deposited: | 08 Jan 2016 10:01 |
| Last Modified: | 21 Nov 2022 11:30 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/56157 |
| DOI: |
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