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Scale invariant version of the original PNN proposed by Specht (1990) <doi:10.1016/0893-6080(90)90049-q> with the added functionality of allowing for smoothing along multiple dimensions while accounting for covariances within the data set. It is written in the R statistical programming language. Given a data set with categorical variables, we use this algorithm to estimate the probabilities of a new observation vector belonging to a specific category. This type of neural network provides the benefits of fast training time relative to backpropagation and statistical generalization with only a small set of known observations.
Version: | 1.2.1 |
Imports: | MASS (≥ 3.1-20), Rcpp (≥ 1.0.0) |
LinkingTo: | Rcpp, RcppArmadillo |
Published: | 2020-01-08 |
DOI: | 10.32614/CRAN.package.spnn |
Author: | Romin Ebrahimi |
Maintainer: | Romin Ebrahimi <romin.ebrahimi at utexas.edu> |
License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
NeedsCompilation: | yes |
Materials: | NEWS |
CRAN checks: | spnn results |
Reference manual: | spnn.pdf |
Package source: | spnn_1.2.1.tar.gz |
Windows binaries: | r-devel: spnn_1.2.1.zip, r-release: spnn_1.2.1.zip, r-oldrel: spnn_1.2.1.zip |
macOS binaries: | r-release (arm64): spnn_1.2.1.tgz, r-oldrel (arm64): spnn_1.2.1.tgz, r-release (x86_64): spnn_1.2.1.tgz, r-oldrel (x86_64): spnn_1.2.1.tgz |
Old sources: | spnn archive |
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