Class Noise Mitigation Through Instance Weighting-Reference-Cited by-同舟云学术

Class Noise Mitigation Through Instance Weighting

Published: Issue: Volume: Page:708-715
ISSN:0302-9743
Container-title:Machine Learning: ECML 2007
language:
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Author:

Rebbapragada Umaa,Brodley Carla E.

Publisher

Springer Berlin Heidelberg

Link

http://link.springer.com/content/pdf/10.1007/978-3-540-74958-5_71.pdf

Reference16 articles.

1. Brodley, C.E., Friedl, M.A.: Identifying mislabeled training data. JAIR 11, 131–167 (1999)

2. Brodley, C.E., Friedl, M.A.: Identifying and eliminating mislabeled training instances. In: Proc. of the 13th National Conference on Artificial Intelligence, pp. 799–805 (1996)

3. Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society 39, 1–38 (1977)

4. Gamberger, D., Lavrač, N., Grošelj, C.: Experiments with noise filtering in a medical domain. In: Proc. of the 16th ICML, pp. 143–151 (1999)

5. Gamberger, D., Lavrač, N., Džeroski, S.: Noise elimination in inductive concept learning: a case study in medical diagnosis. In: 7th Int. Wkshp. on Algorithmic Learning Theory, pp. 199–212 (1996)

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