The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
The algorithm of semi-supervised learning based on finite Gaussian mixture models with a missing-data mechanism is designed for a fitting g-class Gaussian mixture model via maximum likelihood (ML). It is proposed to treat the labels of the unclassified features as missing-data and to introduce a framework for their missing as in the pioneering work of Rubin (1976) for missing in incomplete data analysis. This dependency in the missingness pattern can be leveraged to provide additional information about the optimal classifier as specified by Bayes’ rule.
| Version: | 1.1.1 |
| Depends: | R (≥ 3.1.0), mvtnorm, stats |
| Published: | 2022-10-18 |
| DOI: | 10.32614/CRAN.package.EMMIXSSL |
| Author: | Ziyang Lyu, Daniel Ahfock, Geoffrey J. McLachlan |
| Maintainer: | Ziyang Lyu <ziyang.lyu at unsw.edu.au> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| CRAN checks: | EMMIXSSL results |
| Reference manual: | EMMIXSSL.pdf |
| Package source: | EMMIXSSL_1.1.1.tar.gz |
| Windows binaries: | r-devel: EMMIXSSL_1.1.1.zip, r-release: EMMIXSSL_1.1.1.zip, r-oldrel: EMMIXSSL_1.1.1.zip |
| macOS binaries: | r-release (arm64): EMMIXSSL_1.1.1.tgz, r-oldrel (arm64): EMMIXSSL_1.1.1.tgz, r-release (x86_64): EMMIXSSL_1.1.1.tgz, r-oldrel (x86_64): EMMIXSSL_1.1.1.tgz |
| Old sources: | EMMIXSSL archive |
Please use the canonical form https://CRAN.R-project.org/package=EMMIXSSL to link to this page.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.