✦ LIBER ✦

Probabilistic neural networks

✍ Scribed by Donald F. Specht

Publisher: Elsevier Science
Year: 1990
Tongue: English
Weight: 863 KB
Volume: 3
Category: Article
ISSN: 0893-6080
DOI: 10.1016/0893-6080(90)90049-q

No coin nor oath required. For personal study only.

✦ Synopsis

By replacing the sigmoid activation function often used in neural networks with an exponential function, a probabilistic neural network (PNN) that can compute nonlinear decision boundaries which approach the Bayes optimal is formed. Alternate activation functions having similar properties are also discussed. A fourlayer neural network of the type proposed can map any input pattern to any number of classifications. The decision boundaries can be modified in real-time using new data as they become available, and can be implemented using artificial hardware “neurons” that operate entirely in parallel. Provision is also made for estimating the probability and reliability of a classification as well as making the decision. The technique offers a tremendous speed advantage for problems in which the incremental adaptation time of back propagation is a significant fraction of the total computation time. For one application, the PNN paradigm was 200,000 times faster than back-propagation.

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