pnn: Probabilistic neural networks

The program pnn implements the algorithm proposed by Specht (1990). It is written in the R statistical language. It solves a common problem in automatic learning. Knowing a set of observations described by a vector of quantitative variables, we classify them in a given number of groups. Then, the algorithm is trained with this datasets and should guess afterwards the group of any new observation. This neural network has the main advantage to begin generalization instantaneously even with a small set of known observations. It is delivered with four functions (learn, smooth, perf and guess) and a dataset. The functions are documented with examples and provided with unit tests.

Version: 1.0.1
Suggests: testthat, roxygen2, rgenoud
Published: 2013-05-07
Author: Pierre-Olivier Chasset
Maintainer: Pierre-Olivier Chasset <pierre-olivier at>
License: AGPL
NeedsCompilation: no
Citation: pnn citation info
Materials: README NEWS
CRAN checks: pnn results


Reference manual: pnn.pdf
Package source: pnn_1.0.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X El Capitan binaries: r-release: pnn_1.0.1.tgz
OS X Mavericks binaries: r-oldrel: pnn_1.0.1.tgz
Old sources: pnn archive


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