New self-adaptive probabilistic neural networks in bioinformatic and medical tasks
dc.contributor.author | Georgiou, V. L. | en |
dc.contributor.author | Pavlidis, N. G. | en |
dc.contributor.author | Parsopoulos, K. E. | en |
dc.contributor.author | Alevizos, P. D. | en |
dc.contributor.author | Vrahatis, M. N. | en |
dc.date.accessioned | 2015-11-24T17:01:17Z | |
dc.date.available | 2015-11-24T17:01:17Z | |
dc.identifier.issn | 0218-2130 | - |
dc.identifier.uri | https://olympias.lib.uoi.gr/jspui/handle/123456789/10901 | |
dc.rights | Default Licence | - |
dc.subject | probabilistic neural networks | en |
dc.subject | bioinformatics | en |
dc.subject | particle swarm optimization | en |
dc.subject | particle swarm optimization | en |
dc.subject | learning algorithms | en |
dc.subject | convergence | en |
dc.subject | computation | en |
dc.subject | tests | en |
dc.title | New self-adaptive probabilistic neural networks in bioinformatic and medical tasks | en |
heal.abstract | We propose a self-adaptive probabilistic neural network model, which incorporates optimization algorithms to determine its spread parameters. The performance of the proposed model is investigated on two protein localization problems, as well as on two medical diagnostic tasks. Experimental results are compared with that of feedforward neural networks and support vector machines. Different sampling techniques are used and statistical tests are conducted to calculate the statistical significance of the results. | en |
heal.access | campus | - |
heal.fullTextAvailability | TRUE | - |
heal.journalName | International Journal on Artificial Intelligence Tools | en |
heal.journalType | peer reviewed | - |
heal.language | en | - |
heal.publicationDate | 2006 | - |
heal.recordProvider | Πανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Ηλεκτρονικών Υπολογιστών και Πληροφορικής | el |
heal.type | journalArticle | - |
heal.type.el | Άρθρο Περιοδικού | el |
heal.type.en | Journal article | en |
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