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EVOW
2008
Springer
13 years 6 months ago
Architecture Performance Prediction Using Evolutionary Artificial Neural Networks
The design of computer architectures requires the setting of multiple parameters on which the final performance depends. The number of possible combinations make an extremely huge ...
Pedro A. Castillo, Antonio Miguel Mora, Juan Juli&...
ICANN
2007
Springer
13 years 6 months ago
GARCH Processes with Non-parametric Innovations for Market Risk Estimation
Abstract. A procedure to estimate the parameters of GARCH processes with non-parametric innovations is proposed. We also design an improved technique to estimate the density of hea...
José Miguel Hernández-Lobato, Daniel...
BMCBI
2006
146views more  BMCBI 2006»
13 years 4 months ago
Optimized Particle Swarm Optimization (OPSO) and its application to artificial neural network training
Background: Particle Swarm Optimization (PSO) is an established method for parameter optimization. It represents a population-based adaptive optimization technique that is influen...
Michael Meissner, Michael Schmuker, Gisbert Schnei...
ICANN
2010
Springer
13 years 6 months ago
Classification Based on Multiple-Resolution Data View
Abstract. We examine efficacy of a classifier based on average of kernel density estimators; each estimator corresponds to a different data "resolution". Parameters of th...
Mateusz Kobos, Jacek Mandziuk
LION
2009
Springer
129views Optimization» more  LION 2009»
13 years 11 months ago
Expeditive Extensions of Evolutionary Bayesian Probabilistic Neural Networks
Abstract. Probabilistic Neural Networks (PNNs) constitute a promising methodology for classification and prediction tasks. Their performance depends heavily on several factors, su...
Vasileios L. Georgiou, Sonia Malefaki, Konstantino...