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The algorithm of semi-supervised learning is based on finite Gaussian mixture models and includes a mechanism for handling missing data. It aims to fit a g-class Gaussian mixture model using maximum likelihood. The algorithm treats the labels of unclassified features as missing data, building on the framework introduced by Rubin (1976) <doi:10.2307/2335739> for missing data analysis. By taking into account the dependencies in the missing pattern, the algorithm provides more information for determining the optimal classifier, as specified by Bayes' rule.
| Version: | 1.1.5 |
| Depends: | R (≥ 3.1.0), mvtnorm, stats, methods |
| Published: | 2023-10-16 |
| Author: | Ziyang Lyu [aut, cre], Daniel Ahfock [aut], Ryan Thompson [aut], Geoffrey J. McLachlan [aut] |
| Maintainer: | Ziyang Lyu <ziyang.lyu at unsw.edu.au> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| CRAN checks: | gmmsslm results |
| Reference manual: | gmmsslm.pdf |
| Package source: | gmmsslm_1.1.5.tar.gz |
| Windows binaries: | r-devel: gmmsslm_1.1.5.zip, r-release: gmmsslm_1.1.5.zip, r-oldrel: gmmsslm_1.1.5.zip |
| macOS binaries: | r-release (arm64): gmmsslm_1.1.5.tgz, r-oldrel (arm64): gmmsslm_1.1.5.tgz, r-release (x86_64): gmmsslm_1.1.5.tgz, r-oldrel (x86_64): gmmsslm_1.1.5.tgz |
| Old sources: | gmmsslm archive |
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