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Probabilistic neural-network structure determination for pattern classi (2000)
| Venue: | IEEE Transactions on Neural Networks |
| Citations: | 26 - 0 self |
BibTeX
@ARTICLE{Mao00probabilisticneural-network,
author = {K. Z. Mao and K. -c. Tan and W. Ser},
title = {Probabilistic neural-network structure determination for pattern classi},
journal = {IEEE Transactions on Neural Networks},
year = {2000}
}
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Abstract
Abstract—Network structure determination is an important issue in pattern classification based on a probabilistic neural network. In this study, a supervised network structure determi-nation algorithm is proposed. The proposed algorithm consists of two parts and runs in an iterative way. The first part identifies an appropriate smoothing parameter using a genetic algorithm, while the second part determines suitable pattern layer neurons using a forward regression orthogonal algorithm. The proposed algorithm is capable of offering a fairly small network structure with satisfactory classification accuracy. Index Terms—Genetic algorithms, orthogonal algorithm, pattern classification, probabilistic neural network (PNN). I.
Keyphrases
probabilistic neural-network structure determination pattern classi pattern classification probabilistic neural network second part appropriate smoothing parameter small network structure first part index term genetic algorithm genetic algorithm suitable pattern layer neuron important issue forward regression orthogonal algorithm orthogonal algorithm iterative way supervised network structure determi-nation algorithm satisfactory classification accuracy abstract network structure determination
