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» Training Neural Networks with GA Hybrid Algorithms
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IJCNN
2007
IEEE
14 years 1 months ago
TRUST-TECH Based Neural Network Training
— Efficient Training in a neural network plays a vital role in deciding the network architecture and the accuracy of these classifiers. Most popular local training algorithms t...
Hsiao-Dong Chiang, Chandan K. Reddy
TSMC
2002
119views more  TSMC 2002»
13 years 7 months ago
A cloning approach to classifier training
The Al-Alaoui algorithm is a weighted mean-square error (MSE) approach to pattern recognition. It employs cloning of the erroneously classified samples to increase the population o...
M. A. Al-Alaoui, R. Mouci, M. M. Mansour, Rony Fer...
IJCNN
2006
IEEE
14 years 1 months ago
In Situ Training of CMOL CrossNets
—— Hybrid semiconductor/nanodevice (“CMOL”) technology may allow the implementation of digital and mixed-signal integrated circuits, including artificial neural networks (...
Jung Hoon Lee, Konstantin Likharev
GECCO
2005
Springer
175views Optimization» more  GECCO 2005»
14 years 28 days ago
Nonlinear feature extraction using a neuro genetic hybrid
Feature extraction is a process that extracts salient features from observed variables. It is considered a promising alternative to overcome the problems of weight and structure o...
Yung-Keun Kwon, Byung Ro Moon
JMLR
2010
151views more  JMLR 2010»
13 years 2 months ago
Understanding the difficulty of training deep feedforward neural networks
Whereas before 2006 it appears that deep multilayer neural networks were not successfully trained, since then several algorithms have been shown to successfully train them, with e...
Xavier Glorot, Yoshua Bengio