spnn: Scale Invariant Probabilistic Neural Networks

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: 1.1
Imports: MASS (≥ 3.1-20)
Published: 2018-03-20
Author: Romin Ebrahimi
Maintainer: Romin Ebrahimi <romin.ebrahimi at utexas.edu>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
CRAN checks: spnn results


Reference manual: spnn.pdf
Package source: spnn_1.1.tar.gz
Windows binaries: r-prerel: spnn_1.1.zip, r-release: spnn_1.1.zip, r-oldrel: spnn_1.1.zip
OS X binaries: r-prerel: spnn_1.1.tgz, r-release: spnn_1.1.tgz
Old sources: spnn archive


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