Novel Approaches to Probabilistic Neural Networks Through Bagging and Evolutionary Estimating of Prior Probabilities

Georgiou, V; Alevizos, P; Vrahatis, M

dc.contributor.author	Georgiou, V	en
dc.contributor.author	Alevizos, P	en
dc.contributor.author	Vrahatis, M	en
dc.date.accessioned	2014-03-01T01:56:54Z
dc.date.available	2014-03-01T01:56:54Z
dc.date.issued	2008	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/28274
dc.subject	Benchmark Problem	en
dc.subject	particle swarm optimizer	en
dc.subject	Probabilistic Neural Network	en
dc.title	Novel Approaches to Probabilistic Neural Networks Through Bagging and Evolutionary Estimating of Prior Probabilities	en
heal.type	journalArticle	en
heal.identifier.primary	10.1007/s11063-007-9066-5	en
heal.identifier.secondary	http://dx.doi.org/10.1007/s11063-007-9066-5	en
heal.publicationDate	2008	en
heal.abstract	In this contribution, novel approaches are proposed for the improvement of the performance of Probabilistic Neural Networks as well as the recently proposed Evolutionary Probabilistic Neural Networks. The Evolutionary Probabilistic Neural Network’s matrix of spread parameters is allowed to have different values in each class of neurons, resulting in a more flexible model that fits the data better and Particle	en
heal.journalName	Neural Processing Letters	en
dc.identifier.doi	10.1007/s11063-007-9066-5	en