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» Extracting Propositions from Trained Neural Networks
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IJCNN
2000
IEEE
15 years 1 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 4 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
ESANN
2003
14 years 11 months ago
A new rule extraction algorithm based on interval arithmetic
In this paper we propose a new algorithm for rule extraction from a trained Multilayer Feedforward network. The algorithm is based on an interval arithmetic network inversion for p...
Carlos Hernández-Espinosa, Mercedes Fern&aa...
CVPR
2012
IEEE
12 years 12 months ago
Enhanced continuous sign language recognition using PCA and neural network features
In this work a Gaussian Hidden Markov Model (GHMM) based automatic sign language recognition system is built on the SIGNUM database. The system is trained on appearance-based feat...
Yannick L. Gweth, Christian Plahl, Hermann Ney
FUZZY
2001
Springer
121views Fuzzy Logic» more  FUZZY 2001»
15 years 1 months ago
Interpretation of Trained Neural Networks by Rule Extraction
Vasile Palade, Ciprian-Daniel Neagu, Ronald J. Pat...