Sciweavers

950 search results - page 15 / 190
» Training a Quantum Neural Network
Sort
View
IPPS
1998
IEEE
13 years 12 months ago
Using the BSP Cost Model to Optimise Parallel Neural Network Training
We derive cost formulae for three di erent parallelisation techniques for training supervised networks. These formulae are parameterised by properties of the target computer archit...
R. O. Rogers, David B. Skillicorn
BMCBI
2006
146views more  BMCBI 2006»
13 years 7 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...
ASC
2004
13 years 7 months ago
Extracting rules from trained neural network using GA for managing E-business
Theabilitytointelligentlycollect,manageandanalyzeinformationaboutcustomersandsellersisakeysourceofcompetitive advantage for an e-business. This ability provides an opportunity to ...
Atta Ebrahim E. ElAlfi, R. Haque, M. Esmel ElAlami
INTERSPEECH
2010
13 years 2 months ago
On speaker adaptive training of artificial neural networks
In the paper we present two techniques improving the recognition accuracy of multilayer perceptron neural networks (MLP ANN) by means of adopting Speaker Adaptive Training. The us...
Jan Trmal, Jan Zelinka, Ludek Müller
GECCO
2004
Springer
103views Optimization» more  GECCO 2004»
14 years 1 months ago
Training Neural Networks with GA Hybrid Algorithms
Abstract. Training neural networks is a complex task of great importance in the supervised learning field of research. In this work we tackle this problem with five algorithms, a...
Enrique Alba, J. Francisco Chicano