Identification of unknown categories with probabilistic neural networks

30 December 2002

conference paper
Published by Institute of Electrical and Electronics Engineers (IEEE)

p. 434-437 vol.1
https://doi.org/10.1109/icnn.1993.298596

Abstract

The ability to identify correctly a pattern as an unknown as opposed to misclassifying it as a known category is a desired but often overlooked feature in all neural networks. The method described solves this problem by establishing a threshold on the probability density function (pdf) as determined by a risk strategy. Once sufficient numbers of samples of an unknown category have been collected, it can be added to the existing probabilistic neural network (PNN) classifier as a new category. This online real-time learning technique may be applied to many problems including voice recognition, optical character recognition, automatic target recognition, fault detection, and sonar processing.

Keywords

This publication has 1 reference indexed in Scilit:

Probabilistic neural networks
Neural Networks, 1990