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IEEEICCI
2002
IEEE
14 years 2 months ago
Quasi-Morphism and Comprehensibility of Rules in Inductive Learning
We present a model of creating a hierarchical set of rules that encode generalizations and exceptions derived from induction learning. The rules use the input features directly an...
Wiphada Wettayaprasit, Chidchanok Lursinsap, Chee-...
IJCNN
2007
IEEE
14 years 4 months ago
Using Artificial Neural Networks and Feature Saliency Techniques for Improved Iris Segmentation
—One of the basic challenges to robust iris recognition is iris segmentation. This paper proposes the use of a feature saliency algorithm and an artificial neural network to perf...
Randy P. Broussard, Lauren R. Kennell, David L. So...
CORR
2010
Springer
209views Education» more  CORR 2010»
13 years 10 months ago
An Algorithm to Extract Rules from Artificial Neural Networks for Medical Diagnosis Problems
Artificial neural networks (ANNs) have been successfully applied to solve a variety of classification and function approximation problems. Although ANNs can generally predict bett...
S. M. Kamruzzaman, Md. Monirul Islam
ICML
2008
IEEE
14 years 10 months ago
Classification using discriminative restricted Boltzmann machines
Recently, many applications for Restricted Boltzmann Machines (RBMs) have been developed for a large variety of learning problems. However, RBMs are usually used as feature extrac...
Hugo Larochelle, Yoshua Bengio
JMLR
2006
389views more  JMLR 2006»
13 years 9 months ago
A Very Fast Learning Method for Neural Networks Based on Sensitivity Analysis
This paper introduces a learning method for two-layer feedforward neural networks based on sensitivity analysis, which uses a linear training algorithm for each of the two layers....
Enrique Castillo, Bertha Guijarro-Berdiñas,...