Probabilistic neural network: Difference between revisions

Revision as of 09:33, 10 October 2012

Revision as of 18:54, 7 March 2013

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A '''probabilistic neural network (PNN)''' is a [[feedforward neural network]], which was derived from the [[Bayesian network]]<ref>http://herselfsai.com/2007/03/probabilistic-neural-networks.html</ref> and a statistical algorithm called [[Kernel Fisher discriminant analysis]].<ref>http://www.psi.toronto.edu/~vincent/research/presentations/PNN.pdf</ref> It was introduced by D.F. Specht in the early 1990s.<ref name=Specht1990>{{Cite doi|10.1016/0893-6080(90)90049-Q}}</ref>. In a PNN, the operations are organized into a multilayered feedforward network with four layers:

* Input layer

* Hidden layer

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==Layers of PNN==

=== Input layer ===

Each neuron in the input layer represents a predictor variable. In categorical variables, ''N-1'' neurons are used when there are ''N'' number of categories. It standardizes the range of the values by subtracting the median and dividing by the interquartile range.Then the input neurons feed the values to each of the neurons in the hidden layer.

===Pattern layer===

This layer contains one neuron for each case in the training data set. It stores the values of the predictor variables for the case along with the target value. a hidden neuron computes the Euclidean distance of the test case from the neuron’s center point and then applies the [[Radial_basis_functionRadial basis function|RBF]] kernel function using the sigma values.

===Summation layer===