Probabilistic Active Learning: Uncertainty Sampling
Package and demo details on GitHub
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- This code implements active learning through uncertainty sampling within a Gaussian Mixture Model (GMM), considering applications to streaming data.
- The code was written for engineering applications (structural health monitoring), implementation details can be found at [https://www.sciencedirect.com/science/article/pii/S0888327019305096], a paper published in Mechanical Systems and Signal Processing (MSSP).
Cite As
@article{BULL2019106294, title = "Probabilistic active learning: An online framework for structural health monitoring", journal = "Mechanical Systems and Signal Processing", volume = "134", pages = "106294", year = "2019", author = "L.A. Bull and T.J. Rogers and C. Wickramarachchi and E.J. Cross and K. Worden and N. Dervilis"}
Bull, L.A., Rogers, T.J., Wickramarachchi, C., Cross, E.J., Worden, K. and Dervilis, N., 2019. Probabilistic active learning: An online framework for structural health monitoring. Mechanical Systems and Signal Processing, 134, p.106294.
General Information
- Version 1.0.2 (281 Bytes)
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View License on GitHub
MATLAB Release Compatibility
- Compatible with any release
Platform Compatibility
- Windows
- macOS
- Linux
Versions that use the GitHub default branch cannot be downloaded
| Version | Published | Release Notes | Action |
|---|---|---|---|
| 1.0.2 | citation updates |
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| 1.0.1 | _ |
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| 1.0.0 |
