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Model-Free and Model-Based Active Learning for Regression

Authors: 

Jack O’Neill, Sarah Jane Delany, Brian MacNamee

Publication Type: 
Refereed Original Article
Abstract: 
Training machine learning models often requires large labelled datasets, which can be both expensive and time-consuming to obtain. Active learning aims to selectively choose which data is labelled in order to minimize the total number of labels required to train an effective model. This paper compares model-free and model-based approaches to active learning for regression, finding that model-free approaches, in addition to being less computationally intensive to implement, are more effective in improving the performance of linear regressions than model-based alternatives.
Digital Object Identifer (DOI): 
10.1007/978-3-319-46562-3_24
Publication Status: 
In Press
Date Accepted for Publication: 
Monday, 2 January, 2017
Publication Date: 
02/01/2017
Journal: 
Advances in Computational Intelligence Systems
Volume: 
January 2017
Pages: 
375-386
Institution: 
National University of Ireland, Dublin (UCD)
Open access repository: 
No