Estimation of Classification Rules From Partially Classified Data

Author:

McLachlan Geoffrey,Ahfock Daniel

Publisher

Springer International Publishing

Reference22 articles.

1. Ahfock, D., McLachlan, G.J.: On missing data patterns in semi-supervised learning. ePreprint arXiv:1904.02883 (2019a)

2. Ahfock, D., McLachlan, G.J.: An apparent paradox: a classifier trained from a partially classified sample may have smaller expected error rate than that if the sample were completely classified. ePreprint arXiv:1910.09189v2 (2019b)

3. Cannings, T.I., Fan, Y., Samworth, R.J.: Classification with imperfect training labels. Biometrika 107, 311–330 (2020)

4. Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm (with discussion). J. R. Statist. Soc. B 39, 1–22 (1977)

5. Efron, B.: The efficiency of logistic regression compared to normal discriminant analysis. J. Am. Stat. Assoc. 70, 892–898 (1975)

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