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Training and prediction functions are provided for the Extreme Learning Machine algorithm (ELM). The ELM use a Single Hidden Layer Feedforward Neural Network (SLFN) with random generated weights and no gradient-based backpropagation. The training time is very short and the online version allows to update the model using small chunk of the training set at each iteration. The only parameter to tune is the hidden layer size and the learning function.
| Version: | 1.0 |
| Depends: | R (≥ 3.2.2) |
| Published: | 2015-11-28 |
| Author: | Alessio Petrozziello [aut, cre] |
| Maintainer: | Alessio Petrozziello <alessio.petrozziello at port.ac.uk> |
| License: | GPL-2 | GPL-3 |
| NeedsCompilation: | no |
| CRAN checks: | ELMR results |
| Reference manual: | ELMR.pdf |
| Package source: | ELMR_1.0.tar.gz |
| Windows binaries: | r-devel: ELMR_1.0.zip, r-release: ELMR_1.0.zip, r-oldrel: ELMR_1.0.zip |
| macOS binaries: | r-release (arm64): ELMR_1.0.tgz, r-oldrel (arm64): ELMR_1.0.tgz, r-release (x86_64): ELMR_1.0.tgz, r-oldrel (x86_64): ELMR_1.0.tgz |
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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.