A reduced probabilistic neural network for the classification of large databases

Authors : Abdelhadi LOTFI, Abdelkader BENYETTOU

Pages : 979-989

Doi:10.3906/elk-1209-35

View : 7 | Download : 6

Publication Date : 0000-00-00

Article Type : Research Paper

Abstract :The probabilistic neural network insert ignore into journalissuearticles values(PNN); is a special type of radial basis neural network used mainly for classification problems. Due to the size of the network after training, this type of network is usually used for problems with a small-sized training dataset. In this paper, a new training algorithm is presented for use with large training databases. Application to the handwritten digit database shows that the reduced PNN performs better than the standard PNN for all of the studied cases with a big gain in size and processing speed. This new type of neural network can be used easily for problems with large training databases like biometrics and data mining applications. An extension of the network is possible for new training samples and/or classes without retraining.
Keywords : Classification, pattern recognition, reduced probabilistic neural network, handwritten digit recognition, optimization