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RWNN  

Random Weight Neural Networks
View on CRAN: Click here


Download and install RWNN package within the R console
Install from CRAN:
install.packages("RWNN")

Install from Github:
library("remotes")
install_github("cran/RWNN")

Install by package version:
library("remotes")
install_version("RWNN", "0.4")



Attach the package and use:
library("RWNN")
Maintained by
Søren B. Vilsen
[Scholar Profile | Author Map]
First Published: 2024-09-03
Latest Update: 2024-09-03
Description:
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>.
How to cite:
Søren B. Vilsen (2024). RWNN: Random Weight Neural Networks. R package version 0.4, https://cran.r-project.org/web/packages/RWNN. Accessed 23 Feb. 2025.
Previous versions and publish date:
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Full RWNN package functions and examples
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