Identification of unknown categories with probabilistic neural networks
| Autor: | R.M. Drake, Theodore P. Washburne, D.F. Specht |
|---|---|
| Rok vydání: | 2002 |
| Předmět: |
Artificial neural network
business.industry Computer science Probabilistic logic Pattern recognition Optical character recognition Machine learning computer.software_genre Sonar Fault detection and isolation Probabilistic neural network Automatic target recognition Artificial intelligence business computer Classifier (UML) |
| Zdroj: | ICNN |
| DOI: | 10.1109/icnn.1993.298596 |
| Popis: | 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. > |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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