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» Interpretation of Trained Neural Networks by Rule Extraction
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IJCNN
2000
IEEE
15 years 4 months ago
Sensitivity Analysis for Conic Section Function Neural Networks
Sensitivity analysis is a method for extracting the cause and effect relationship between the inputs and outputs of the network. After training a neural network, one may want to k...
Lale Özyilmaz, Tülay Yildirim
TNN
2010
234views Management» more  TNN 2010»
14 years 6 months 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
TSMC
1998
139views more  TSMC 1998»
14 years 11 months ago
A neuro-fuzzy controller for mobile robot navigation and multirobot convoying
—A Neural integrated Fuzzy conTroller (NiF-T) which integrates the fuzzy logic representation of human knowledge with the learning capability of neural networks is developed for ...
Kim C. Ng, Mohan M. Trivedi
TNN
1998
92views more  TNN 1998»
14 years 11 months ago
Inductive inference from noisy examples using the hybrid finite state filter
—Recurrent neural networks processing symbolic strings can be regarded as adaptive neural parsers. Given a set of positive and negative examples, picked up from a given language,...
Marco Gori, Marco Maggini, Enrico Martinelli, Giov...
ICPR
2010
IEEE
15 years 4 months ago
Learning Sparse Face Features : Application to Face Verification
We present a low resolution face recognition technique based on a Convolutional Neural Network approach. The network is trained to reconstruct a reference per subject image. In cl...
Pierre Buyssens, Marinette Revenu