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.
||MASS (≥ 3.1-20), Rcpp (≥ 1.0.0)
||Romin Ebrahimi <romin.ebrahimi at utexas.edu>
||GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
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