Package: RWNN 0.4
RWNN: Random Weight Neural Networks
Creation, estimation, and prediction of random weight neural networks (RWNN), Schmidt et al. (1992) <doi:10.1109/ICPR.1992.201708>, including popular variants like extreme learning machines, Huang et al. (2006) <doi:10.1016/j.neucom.2005.12.126>, sparse RWNN, Zhang et al. (2019) <doi:10.1016/j.neunet.2019.01.007>, and deep RWNN, Henríquez et al. (2018) <doi:10.1109/IJCNN.2018.8489703>. It further allows for the creation of ensemble RWNNs like bagging RWNN, Sui et al. (2021) <doi:10.1109/ECCE47101.2021.9595113>, boosting RWNN, stacking RWNN, and ensemble deep RWNN, Shi et al. (2021) <doi:10.1016/j.patcog.2021.107978>.
Authors:
RWNN_0.4.tar.gz
RWNN_0.4.zip(r-4.5)RWNN_0.4.zip(r-4.4)RWNN_0.4.zip(r-4.3)
RWNN_0.4.tgz(r-4.4-x86_64)RWNN_0.4.tgz(r-4.4-arm64)RWNN_0.4.tgz(r-4.3-x86_64)RWNN_0.4.tgz(r-4.3-arm64)
RWNN_0.4.tar.gz(r-4.5-noble)RWNN_0.4.tar.gz(r-4.4-noble)
RWNN_0.4.tgz(r-4.4-emscripten)RWNN_0.4.tgz(r-4.3-emscripten)
RWNN.pdf |RWNN.html✨
RWNN/json (API)
# Install 'RWNN' in R: |
install.packages('RWNN', repos = c('https://svilsen.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/svilsen/rwnn/issues
- example_data - Example data
Last updated 3 months agofrom:ace4027904. Checks:OK: 3 NOTE: 6. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 03 2024 |
R-4.5-win-x86_64 | OK | Nov 03 2024 |
R-4.5-linux-x86_64 | OK | Nov 03 2024 |
R-4.4-win-x86_64 | NOTE | Nov 03 2024 |
R-4.4-mac-x86_64 | NOTE | Nov 03 2024 |
R-4.4-mac-aarch64 | NOTE | Nov 03 2024 |
R-4.3-win-x86_64 | NOTE | Nov 03 2024 |
R-4.3-mac-x86_64 | NOTE | Nov 03 2024 |
R-4.3-mac-aarch64 | NOTE | Nov 03 2024 |
Exports:ae_rwnnbag_rwnnboost_rwnnclassifycontrol_rwnned_rwnnreduce_networkrwnnstack_rwnn
Dependencies:quadprograndtoolboxRcppRcppArmadillorngWELL
Readme and manuals
Help Manual
Help page | Topics |
---|---|
Auto-encoder pre-trained random weight neural networks | ae_rwnn ae_rwnn.formula |
Bagging random weight neural networks | bag_rwnn bag_rwnn.formula |
Boosting random weight neural networks | boost_rwnn boost_rwnn.formula |
Classifier | classify |
rwnn control function | control_rwnn |
Ensemble deep random weight neural networks | ed_rwnn ed_rwnn.formula |
An ERWNN-object | ERWNN-object |
Example data | example_data |
Predicting targets of an ERWNN-object | predict.ERWNN |
Predicting targets of an RWNN-object | predict.RWNN |
Reduce the weights of a random weight neural network. | reduce_network reduce_network.ERWNN reduce_network.RWNN |
Random weight neural networks | rwnn rwnn.formula |
An RWNN-object | RWNN-object |
Stacking random weight neural networks | stack_rwnn stack_rwnn.formula |