Expeditive Extensions of Evolutionary Bayesian Probabilistic Neural Networks

Georgiou, V; Malefaki, S; Parsopoulos, K; Alevizos, P; Vrahatis, M

dc.contributor.author	Georgiou, V	en
dc.contributor.author	Malefaki, S	en
dc.contributor.author	Parsopoulos, K	en
dc.contributor.author	Alevizos, P	en
dc.contributor.author	Vrahatis, M	en
dc.date.accessioned	2014-03-01T01:57:49Z
dc.date.available	2014-03-01T01:57:49Z
dc.date.issued	2009	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/28544
dc.subject	bayesian model	en
dc.subject	Benchmark Problem	en
dc.subject	Classification Accuracy	en
dc.subject	particle swarm optimizer	en
dc.subject	Perforation	en
dc.subject	Probabilistic Neural Network	en
dc.title	Expeditive Extensions of Evolutionary Bayesian Probabilistic Neural Networks	en
heal.type	journalArticle	en
heal.identifier.primary	10.1007/978-3-642-11169-3_3	en
heal.identifier.secondary	http://dx.doi.org/10.1007/978-3-642-11169-3_3	en
heal.publicationDate	2009	en
heal.abstract	Probabilistic Neural Networks (PNNs) constitute a promis- ing methodology for classification and prediction tasks. Their perfor- mance depends heavily on several factors, such as their spread param- eters, kernels, and prior probabilities. Recently, Evolutionary Bayesian PNNs were proposed to address this problem by incorporating Bayesian models for estimation of spread parameters, as well as Particle Swarm Optimization (PSO) as a	en
dc.identifier.doi	10.1007/978-3-642-11169-3_3	en