Scale Invariant Probabilistic Neural Networks

Scale invariant version of the original PNN proposed by Specht (1990) 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.


Reference manual

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1.2.1 by Romin Ebrahimi, a year ago

Browse source code at

Authors: Romin Ebrahimi

Documentation:   PDF Manual  

GPL (>= 2) license

Imports MASS, Rcpp

Linking to Rcpp, RcppArmadillo

See at CRAN