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Robust mixture discriminant analysis (RMDA), proposed in Bouveyron & Girard, 2009 <doi:10.1016/j.patcog.2009.03.027>, allows to build a robust supervised classifier from learning data with label noise. The idea of the proposed method is to confront an unsupervised modeling of the data with the supervised information carried by the labels of the learning data in order to detect inconsistencies. The method is able afterward to build a robust classifier taking into account the detected inconsistencies into the labels.
| Version: | 1.2 |
| Depends: | MASS, mclust, Rsolnp |
| Published: | 2020-10-14 |
| Author: | Charles Bouveyron & Stephane Girard |
| Maintainer: | Charles Bouveyron <charles.bouveyron at gmail.com> |
| License: | GPL-2 |
| NeedsCompilation: | no |
| In views: | Robust |
| CRAN checks: | robustDA results |
| Reference manual: | robustDA.pdf |
| Package source: | robustDA_1.2.tar.gz |
| Windows binaries: | r-devel: robustDA_1.2.zip, r-release: robustDA_1.2.zip, r-oldrel: robustDA_1.2.zip |
| macOS binaries: | r-release (arm64): robustDA_1.2.tgz, r-oldrel (arm64): robustDA_1.2.tgz, r-release (x86_64): robustDA_1.2.tgz, r-oldrel (x86_64): robustDA_1.2.tgz |
| Old sources: | robustDA archive |
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