Document Type

Theses, Masters

Rights

This item is available under a Creative Commons License for non-commercial use only

Disciplines

Computer Sciences

Publication Details

Dublin Institute of Technology, 2015.

Abstract

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

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