Probabilistic neural network: Difference between revisions

Revision as of 08:05, 22 March 2012

Revision as of 08:10, 22 March 2012

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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.

== Advantages== and disadvantages ==

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.

* PNN are more accurate than multilayer perceptron networks.

* PNN are networks are relatively insensitive to outliers.

* PNN networks generate accurate predicted target probability scores.

* PNN approach Bayes optimal classification.

* Bulleted list item

==disadvantages ==

* PNN are slower than multilayer perceptron networks at classifying new cases.

* PNN require more memory space to store the model

== References ==