Baseline Methods for Active Learning

Research output: Chapter in Book/Report/Conference proceedingChapter


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.
Original languageEnglish
Title of host publicationJMLR: Workshop and Conference Proceedings 16
Subtitle of host publicationWorkshop on Active Learning and Experimental Design
EditorsI Guyon, G Cawley, G Dror, V Lemaire, A Statnikov
Number of pages11
Publication statusPublished - 2011

Publication series

NameJMLR Workshop and Conference Proceedings

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