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» Training Methods for Adaptive Boosting of Neural Networks
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IJCNN
2006
IEEE
15 years 3 months ago
Methods for Parallelizing the Probabilistic Neural Network on a Beowulf Cluster Computer
—In this paper, we present three different methods for implementing the Probabilistic Neural Network on a Beowulf cluster computer. The three methods, Parallel Full Training Set ...
Jimmy Secretan, Michael Georgiopoulos, Ian Maidhof...
73
Voted
IJCNN
2006
IEEE
15 years 3 months ago
An Adaptive Penalty-Based Learning Extension for Backpropagation and its Variants
Abstract— Over the years, many improvements and refinements of the backpropagation learning algorithm have been reported. In this paper, a new adaptive penalty-based learning ex...
Boris Jansen, Kenji Nakayama
IJON
2007
118views more  IJON 2007»
14 years 9 months ago
Time series prediction with recurrent neural networks trained by a hybrid PSO-EA algorithm
To predict the 100 missing values from a time series of 5000 data points, given for the IJCNN 2004 time series prediction competition, recurrent neural networks (RNNs) are trained...
Xindi Cai, Nian Zhang, Ganesh K. Venayagamoorthy, ...
ICANN
1997
Springer
15 years 1 months ago
Local Subspace Classifier
Subsystems for on-line recognition of handwriting are needed in personal digital assistants (PDAs) and other portable handheld devices. We have developed a recognition system whic...
Jorma Laaksonen
IJON
2006
138views more  IJON 2006»
14 years 9 months ago
Time-series prediction using a local linear wavelet neural network
A local linear wavelet neural network (LLWNN) is presented in this paper. The difference of the network with conventional wavelet neural network (WNN) is that the connection weigh...
Yuehui Chen, Bo Yang, Jiwen Dong