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» Training Methods for Adaptive Boosting of Neural Networks
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TNN
2008
177views more  TNN 2008»
13 years 7 months ago
Adaptive Importance Sampling to Accelerate Training of a Neural Probabilistic Language Model
Previous work on statistical language modeling has shown that it is possible to train a feed-forward neural network to approximate probabilities over sequences of words, resulting...
Yoshua Bengio, Jean-Sébastien Senecal
ESANN
2003
13 years 8 months ago
Ensemble of hybrid networks with strong regularization
Abstract. We study various ensemble methods for hybrid neural networks. The hybrid networks are composed of radial and projection units and are trained using a deterministic algori...
Shimon Cohen, Nathan Intrator
WSC
2004
13 years 8 months ago
Adaptive Wavelet Neural Network for Prediction of Hourly NOx and NO2 Concentrations
Adaptive neural network is a powerful tool for prediction of air pollution abatement scenarios. But it is often difficult to avoid overfit during the training of adaptive neural n...
Zhiguo Zhang, Ye San
EOR
2000
77views more  EOR 2000»
13 years 7 months ago
Training the random neural network using quasi-Newton methods
Training in the random neural network (RNN) is generally speci
Aristidis Likas, Andreas Stafylopatis
AMC
2007
154views more  AMC 2007»
13 years 7 months ago
A hybrid particle swarm optimization-back-propagation algorithm for feedforward neural network training
The particle swarm optimization algorithm was showed to converge rapidly during the initial stages of a global search, but around global optimum, the search process will become ve...
Jing-Ru Zhang, Jun Zhang, Tat-Ming Lok, Michael R....