Optimizing the Performance of Probabilistic Neural Networks in a Bionformatics Task

Georgiou, V; Pavlidis, N; Parsopoulos, K; Alevizos, D; Vrahatis, M

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