Probabilistic neural network: Difference between revisions

Revision as of 09:25, 22 March 2012

Revision as of 16:40, 6 April 2012

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A '''Probabilistic Neural Network (PNN)''' is a [[Feedforward neural network]] , which was derived from [[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. In a PNN, the operations are organized into a multilayered feedforward network with four layers:

* Input layer

* Hidden layer

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For PNN networks there is one pattern neuron for each category of the target variable. The actual target category of each training case is stored with each hidden neuron; the weighted value coming out of a hidden neuron is fed only to the pattern neuron that corresponds to the hidden neuron’s category. The pattern neurons add the values for the class they represent

PNN often use in classification problems.<ref>http://www.mathworks.in/help/toolbox/nnet/ug/bss38ji-1.html</ref>.When an Input is present, first layer computes the distance from the input vector to the training input vectors. It produce a vector where its elements indicate how close the input is to training input. The second layer sums the contribution for each class of inputs and produce it's net output as a vector of probabilities.Finally, a compete transfer function on the output of the second layer picks the maximum of these probabilities, and produces a 1 for that class and a 0 for the other classes.

===Output layer===

The output layer compares the weighted votes for each target category accumulated in the pattern layer and uses the largest vote to predict the target category.

== Advantages==

There are several advantages and disadvantages using PNN instead of [[multilayer perceptron]].<ref>http://www.dtreg.com/pnn.htm</ref>

* PNN is much faster compare to multilayer perceptron networks.

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== References ==

{{Reflist}}

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