@inproceedings{383ba178a95349909e4fa6a64edc6929,
title = "Model-free and model-based active learning for regression",
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.",
keywords = "Training machine learning models, large labelled datasets, expensive, time-consuming, Active learning, selectively choose, minimize labels, effective model, model-free, model-based, regression, computationally intensive, linear regressions",
author = "Jack O{\textquoteright}Neill and Delany, {Sarah Jane} and Brian Macnamee",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 16th UK Workshop on Computational Intelligence, UKCI 2016 ; Conference date: 07-09-2016 Through 09-09-2016",
year = "2017",
doi = "10.1007/978-3-319-46562-3_24",
language = "English",
isbn = "9783319465616",
series = "Advances in Intelligent Systems and Computing",
publisher = "Springer Verlag",
pages = "375--386",
editor = "Alexander Gegov and Chrisina Jayne and Qiang Shen and Plamen Angelov",
booktitle = "Advances in Computational Intelligence Systems - Contributions Presented at the 16th UK Workshop on Computational Intelligence, 2016",
address = "Germany",
}