!!! Overview
[{$pagename}] ([PNN]) is a [Feedforward Neural network] which is widely used in [classification] and [pattern-recognition] problems. 

[{$pagename}] use an [algorithm], the parent Probability density function (PDF) of each [Classification] is approximated by a Parzen window and a non-parametric [function]. Then, using PDF of each class, the class probability of a new input data is estimated and Bayes’ rule is then employed to allocate the class with highest posterior probability to new input data. By this method, the probability of mis-classification is minimized. This type of [Artificial Neural network] was derived from the Bayesian network and a statistical algorithm called Kernel Fisher discriminant analysis. 

 ∫

[{$pagename}] organizes the operations into a multilayered [Feedforward Neural network] with four layers:

* [Input layer]
* [Hidden layer]
* [Summation layer]
* [Output layer]

!! Category
%%category [Artificial Intelligence]%%

!! More Information
There might be more information for this subject on one of the following:
[{ReferringPagesPlugin before='*' after='\n' }]
----
* [#1] - [Probability_density_function|Wikipedia:Probability_density_function|target='_blank'] - based on information obtained 2017-12-28-