A probabilistic neural network (PNN)[1] is a feedforward neural network, 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 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.[2] This type of artificial neural network (ANN) was derived from the Bayesian network[3] and a statistical algorithm called Kernel Fisher discriminant analysis.[4] It was introduced by D.F. Specht in 1966.[5][6] In a PNN, the operations are organized into a multilayered feedforward network with four layers:
Input layer
Pattern layer
Summation layer
Output layer
^Mohebali, Behshad; Tahmassebi, Amirhessam; Meyer-Baese, Anke; Gandomi, Amir H. (2020). Probabilistic neural networks: a brief overview of theory, implementation, and application. Elsevier. pp. 347–367. doi:10.1016/B978-0-12-816514-0.00014-X. S2CID 208119250.
^"Probabilistic Neural Networks". Archived from the original on 2010-12-18. Retrieved 2012-03-22.
^"Archived copy" (PDF). Archived from the original (PDF) on 2012-01-31. Retrieved 2012-03-22.{{cite web}}: CS1 maint: archived copy as title (link)
^Specht, D. F. (1967-06-01). "Generation of Polynomial Discriminant Functions for Pattern Recognition". IEEE Transactions on Electronic Computers. EC-16 (3): 308–319. doi:10.1109/PGEC.1967.264667. ISSN 0367-7508.
^Specht, D. F. (1990). "Probabilistic neural networks". Neural Networks. 3: 109–118. doi:10.1016/0893-6080(90)90049-Q.
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