https://github.com/wmkouw/tcpr
Target contrastive pessimistic risk minimization
https://github.com/wmkouw/tcpr
domain-adaptation machine-learning risk-minimization robust
Last synced: 2 months ago
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Target contrastive pessimistic risk minimization
- Host: GitHub
- URL: https://github.com/wmkouw/tcpr
- Owner: wmkouw
- License: mit
- Created: 2016-01-30T21:31:42.000Z (over 9 years ago)
- Default Branch: master
- Last Pushed: 2022-01-10T14:12:02.000Z (over 3 years ago)
- Last Synced: 2025-02-10T00:48:16.373Z (4 months ago)
- Topics: domain-adaptation, machine-learning, risk-minimization, robust
- Language: MATLAB
- Homepage:
- Size: 898 KB
- Stars: 0
- Watchers: 3
- Forks: 1
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Target contrastive pessimistic risk
This repository accompanies the paper:
"Robust domain-adaptive discriminant analysis"
published in Pattern Recognition Letters, vol. 248, pp 107-113, 2021 ([doi](https://doi.org/10.1016/j.patrec.2021.05.005)).
## Installation
Download:
- Junfeng Wens's Robust Covariate Shift Adjustment: https://webdocs.cs.ualberta.ca/~jwen4/codes/RobustLearning.zip
- Mark Schmidt's minFunc: http://www.cs.ubc.ca/~schmidtm/Software/minFunc.html
Add the unzipped folders to your path.
## Usage
Each folder marked __experiment\*__, contains a script starting with __run_exp\*__. It calls a function that contains experimental parameters, such as which classifiers to test, and runs the experiment. Results will be stored in a new folder. These can be gathered and printed by using the function __gather_exp\*__.
### Contact
Questions, bugs, and general feedback can be submitted to the [issues tracker](https://github.com/wmkouw/tcpr/issues).