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Optimizing the Performance of Probabilistic Neural Networks in a Bionformatics Task

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dc.contributor.author Georgiou, V en
dc.contributor.author Pavlidis, N en
dc.contributor.author Parsopoulos, K en
dc.contributor.author Alevizos, D en
dc.contributor.author Vrahatis, M en
dc.date.accessioned 2014-03-01T01:53:29Z
dc.date.available 2014-03-01T01:53:29Z
dc.date.issued 2004 en
dc.identifier.uri https://dspace.lib.ntua.gr/xmlui/handle/123456789/27033
dc.relation.uri http://www.math.upatras.gr/~npav/papers/GPPAV_PNN.pdf en
dc.subject particle swarm optimizer en
dc.subject Perforation en
dc.subject Probabilistic Neural Network en
dc.subject Sampling Technique en
dc.subject Statistical Test en
dc.subject Neural Network en
dc.subject Particle Swarm Optimization Algorithm en
dc.title Optimizing the Performance of Probabilistic Neural Networks in a Bionformatics Task en
heal.type journalArticle en
heal.publicationDate 2004 en
heal.abstract A self adaptive probabilistic neural network model is proposed. The model incorporates the Particle Swarm Optimization algorithm to optimize the spread parameter of the probabilistic neural network, enhancing thus its perfor- mance. The proposed approach is tested on two data sets from the eld of bioinformatics, with promising results. The performance of the proposed model is compared to probabilistic neural en


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