Biquality learning: a framework to design algorithms dealing with closed-set distribution shifts
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Link
https://link.springer.com/content/pdf/10.1007/s10994-023-06372-3.pdf
Reference46 articles.
1. Bickel, S., Brückner, M., Scheffer, T. (2007). Discriminative learning for differing training and test distributions. In: Proceedings of the 24th international conference on Machine learning, pp 81–88.
2. Boser, B.E., Guyon, I.M., Vapnik, V.N. (1992). A training algorithm for optimal margin classifiers. In: Proceedings of the fifth annual workshop on Computational learning theory, pp 144–152.
3. Breiman, L. (1984). Classification and regression trees. Routledge.
4. Chang, C. C., & Lin, C. J. (2011). Libsvm: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST), 2(3), 1–27.
5. Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20(1), 37–46.
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