Bayesian active learning for maximal information gain on model parameters
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Documents
- Fulltext
Accepted author manuscript, 4.39 MB, PDF document
The fact that machine learning models, despite their advancements, are still trained on randomly gathered data is proof that a lasting solution to the problem of optimal data gathering has not yet been found. In this paper, we investigate whether a Bayesian approach to the classification problem can provide assumptions under which one is guaranteed to perform at least as good as random sampling. For a logistic regression model, we show that maximal expected information gain on model parameters is a promising criterion for selecting samples, assuming that our classification model is well-matched to the data. Our derived criterion is closely related to the maximum model change. We experiment with data sets which satisfy this assumption to varying degrees to see how sensitive our performance is to the violation of our assumption in practice.
Original language | English |
---|---|
Title of host publication | Proceedings of ICPR 2020 - 25th International Conference on Pattern Recognition |
Number of pages | 8 |
Publisher | IEEE |
Publication date | 2020 |
Pages | 10524-10531 |
Article number | 9411962 |
ISBN (Electronic) | 9781728188089 |
DOIs | |
Publication status | Published - 2020 |
Event | 25th International Conference on Pattern Recognition, ICPR 2020 - Virtual, Milan, Italy Duration: 10 Jan 2021 → 15 Jan 2021 |
Conference
Conference | 25th International Conference on Pattern Recognition, ICPR 2020 |
---|---|
Land | Italy |
By | Virtual, Milan |
Periode | 10/01/2021 → 15/01/2021 |
Number of downloads are based on statistics from Google Scholar and www.ku.dk
ID: 286999036