Probabilistic neural network

<p>A probabilistic neural network (PNN) is a <a href="/facts/Feedforward_neural_network/CP0pPGDF">feedforward neural network</a>, which is widely used in classification and pattern recognition problems. In the PNN algorithm, the parent probability distribution function (PDF) of each class is approximated by a <a href="/facts/Kernel_density_estimation/NmznyEW8">Parzen window</a> 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 <a href="/facts/Artificial_neural_network/6V1jMlkx">artificial neural network</a> (ANN) was derived from the <a href="/facts/Bayesian_network/Ry31vq6T">Bayesian network</a> and a statistical algorithm called <a href="/facts/Kernel_Fisher_discriminant_analysis/pNdskU0A">Kernel Fisher discriminant analysis</a>. It was introduced by D.F. Specht in 1966. In a PNN, the operations are organized into a multilayered feedforward network with four layers:
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<ul><li>Input layer</li>
<li>Pattern layer</li>
<li>Summation layer</li>
<li>Output layer</li></ul>

Probabilistic neural network open-in-new

Probabilistic neural network