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Title: Active learning using localized generalization error of candidate sample as criterion
Authors: Chan, APF
Yeung, DS
Keywords: Learning (artificial intelligence)
Issue Date: 2005
Publisher: IEEE
Source: 2005 IEEE International Conference on Systems, Man and Cybernetics, 10-12 October 2005, v. 4, p. 3604-3609 How to cite?
Abstract: In classification problem, the learning process can be more efficient if the informative samples can be selected actively based on the knowledge of the classifier. This problem is called active learning. Most of the existing active learning methods did not directly relate to the generalization error of classifiers. Also, some of them need high computational time or are based on strict assumptions. This paper describes a new active learning strategy using the concept of localized generalization error of the candidate samples. The sample which yields the largest generalization error will be chosen for query. This method can be applied to different kinds of classifiers and its complexity is low. Experimental results demonstrate that the prediction accuracy of the classifier can be improved by using this selecting method and fewer training samples are possible for the same prediction accuracy.
ISBN: 0-7803-9298-1
DOI: 10.1109/ICSMC.2005.1571707
Appears in Collections:Conference Paper

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