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.
|Suggests:||testthat, roxygen2, rgenoud|
|Maintainer:||Pierre-Olivier Chasset <pierre-olivier at chasset.net>|
|Citation:||pnn citation info|
|CRAN checks:||pnn results|
|Windows binaries:||r-devel: pnn_1.0.1.zip, r-release: pnn_1.0.1.zip, r-oldrel: pnn_1.0.1.zip|
|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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