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An Evaluation of Selection Strategies for Active Learning with Regression
O'Neill, Jack
While active learning for classification problems has received considerable attention in recent years, studies on problems of regression are rare. This paper provides a systematic review of the most commonly used selection strategies for active learning within the context of linear regression. The recently developed Exploration Guided Active Learning (EGAL) algorithm, previously deployed within a classification context, is explored as a selection strategy for regression problems. Active learning is demonstrated to significantly improve the learning rate of linear regression models. Experimental results show that a purely diversity-based approach to
Keyword(s): Active Learning; Regression; EGAL; Hypothesis Testing; Diversity; Computer Engineering
Publication Date:
Type: Other
Peer-Reviewed: Unknown
Institution: Dublin Institute of Technology
Citation(s): Dissertations
Publisher(s): Dublin Institute of Technology
File Format(s): application/pdf
First Indexed: 2015-10-10 05:35:27 Last Updated: 2017-12-14 07:05:32