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» Training Methods for Adaptive Boosting of Neural Networks
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TNN
2010
234views Management» more  TNN 2010»
13 years 1 days ago
Novel maximum-margin training algorithms for supervised neural networks
This paper proposes three novel training methods, two of them based on the back-propagation approach and a third one based on information theory for Multilayer Perceptron (MLP) bin...
Oswaldo Ludwig, Urbano Nunes
IWANN
2009
Springer
13 years 12 months ago
Ensemble Methods for Boosting Visualization Models
Bruno Baruque, Emilio Corchado, Aitor Mata, Juan M...
IJCNN
2006
IEEE
13 years 11 months ago
Effective Training Methods for Function Localization Neural Networks
— Inspired by Hebb’s cell assembly theory about how the brain worked, we have developed a function localization neural network (FLNN). The main part of a FLNN is structurally t...
Takafumi Sasakawa, Jinglu Hu, Katsunori Isono, Kot...
NIPS
1994
13 years 6 months ago
Boosting the Performance of RBF Networks with Dynamic Decay Adjustment
Radial Basis Function (RBF) Networks, also known as networks of locally{tuned processing units (see 6]) are well known for their ease of use. Most algorithms used to train these t...
Michael R. Berthold, Jay Diamond
ENVSOFT
2008
115views more  ENVSOFT 2008»
13 years 5 months ago
Adaptive fuzzy modeling versus artificial neural networks
In this paper two areas of soft computing (fuzzy modeling and artificial neural networks) are discussed. Based on the fundamental mathematical similarity of fuzzy technique and ra...
Ralf Wieland, Wilfried Mirschel