Zobrazeno 1 - 10
of 12
pro vyhledávání: '"Assem Kaylani"'
Publikováno v:
Nonlinear Analysis: Theory, Methods & Applications. 73:1783-1791
The probabilistic neural network (PNN) is a neural network architecture that approximates the functionality of the Bayesian classifier, the optimal classifier. Designing the optimal Bayesian classifier is infeasible in practice, since the distributio
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b4f3bfda95733505113af40dda62eda4
https://doi.org/10.1016/j.na.2010.04.080
https://doi.org/10.1016/j.na.2010.04.080
Publikováno v:
Neurocomputing. 72:2079-2092
This paper focuses on classification problems, and in particular on the evolution of ARTMAP architectures using genetic algorithms, with the objective of improving generalization performance and alleviating the adaptive resonance theory (ART) categor
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::523276802a6a9d7102ac8dc8764c1194
https://doi.org/10.1016/j.neucom.2008.09.016
https://doi.org/10.1016/j.neucom.2008.09.016
Autor:
Martin J. Steele, Sam Fayez, Dayana Cope, Mansooreh Mollaghasemi, Assem Kaylani, Ghaith Rabadi
Publikováno v:
Journal of the Operational Research Society. 59:1312-1320
Several NASA programs have been established to study and improve the current launch capability to meet the need for more aggressive space exploration in the future. Numerous launch systems have been proposed by different government and commercial org
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f22fe0d2b2ee5c505911c3435245209e
https://doi.org/10.1057/palgrave.jors.2602478
https://doi.org/10.1057/palgrave.jors.2602478
Publikováno v:
Journal of Simulation. 2:41-52
Airport terminals have dramatically changed after September 11th, primarily due to the tightened security measures. These changes had a major impact on passenger arrival patterns, passenger flows, space allocation, processing times, and waiting times
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::07e3a7d74e2aec6cf329aa1bc322cf7e
https://doi.org/10.1057/palgrave.jos.4250030
https://doi.org/10.1057/palgrave.jos.4250030
Autor:
Ahmad Al-Daraiseh, Michael Georgiopoulos, Mansooreh Mollaghasemi, Assem Kaylani, Annie S. Wu, Georgios C. Anagnostopoulos
Publikováno v:
Neural Networks. 20:874-892
This paper focuses on the evolution of Fuzzy ARTMAP neural network classifiers, using genetic algorithms, with the objective of improving generalization performance (classification accuracy of the ART network on unseen test data) and alleviating the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::54097b02be807569e861e684fa6b6bcc
https://doi.org/10.1016/j.neunet.2007.05.006
https://doi.org/10.1016/j.neunet.2007.05.006
Publikováno v:
IJCNN
In this paper the major principles to effectively design a parameter-less, multi-objective evolutionary algorithm that optimizes a population of probabilistic neural network (PNN) classifier models are articulated; PNN is an example of an exemplar-ba
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ae2d08c2135a9c1015999bbfe172ed80
https://doi.org/10.1109/ijcnn.2011.6033432
https://doi.org/10.1109/ijcnn.2011.6033432
Publikováno v:
IEEE Congress on Evolutionary Computation
In this paper we present a novel framework for evolving ART-based classification models, which we refer to as MOME-ART. The new training framework aims to evolve populations of ART classifiers to optimize both their classification error and their str
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::95653ed356df10c21e3b708629c18a39
https://doi.org/10.1109/cec.2010.5586385
https://doi.org/10.1109/cec.2010.5586385
Autor:
C.G. Sentelle, Georgios C. Anagnostopoulos, Mingyu Zhong, Michael Georgiopoulos, Mansooreh Mollaghasemi, Assem Kaylani
Publikováno v:
IEEE transactions on neural networks. 21(4)
In this paper, we present the evolution of adaptive resonance theory (ART) neural network architectures (classifiers) using a multiobjective optimization approach. In particular, we propose the use of a multiobjective evolutionary approach to simulta
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8994b7c6e22437e8de04ede50338bed6
https://pubmed.ncbi.nlm.nih.gov/20172827
https://pubmed.ncbi.nlm.nih.gov/20172827
Publikováno v:
IEEE Congress on Evolutionary Computation
In this work we present, for the first time, the evolution of ART Neural Network architectures (classifiers) using a multiobjective optimization approach. In particular, we propose the use of a multiobjective evolutionary approach to evolve simultane
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2b27bcd8aea4a2b80e79f0580ea3b595
https://doi.org/10.1109/cec.2008.4630981
https://doi.org/10.1109/cec.2008.4630981
Autor:
Ahmad Al-Daraiseh, Michael Georgiopoulos, Assem Kaylani, Georgios C. Anagnostopoulos, Annie S. Wu, Mansooreh Mollaghasemi
Publikováno v:
IJCNN
This paper focuses on the evolution of ARTMAP architectures, using genetic algorithms, with the objective of improving generalization performance and alleviating the ART category proliferation problem. We refer to the resulting architectures as GFAM,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::148991573c58bfc49926622bf0f32265
https://doi.org/10.1109/ijcnn.2007.4370986
https://doi.org/10.1109/ijcnn.2007.4370986