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» Extraction of Symbolic Rules from Artificial Neural Networks
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ESANN
1997
13 years 8 months ago
Extraction of crisp logical rules using constrained backpropagation networks
Two recently developed methods for extraction of crisp logical rules from neural networks trained with backpropagation algorithm are compared. Both methods impose constraints on th...
Wlodzislaw Duch, Rafal Adamczak, Krzysztof Grabcze...
AIA
2006
13 years 9 months ago
Recurrent and Concurrent Neural Networks for Objects Recognition
A system based on a neural network framework is considered. We used two neural networks, an Elman network [1][2] and a Kohonen (concurrent) network [3], for a categorization task....
Federico Cecconi, Marco Campenní
ICTAI
2009
IEEE
14 years 2 months ago
Probabilistic Neural Logic Network Learning: Taking Cues from Neuro-Cognitive Processes
This paper describes an attempt to devise a knowledge discovery model that is inspired from the two theoretical frameworks of selectionism and constructivism in human cognitive le...
Henry Wai Kit Chia, Chew Lim Tan, Sam Yuan Sung
CIBCB
2005
IEEE
13 years 9 months ago
Neuro-fuzzy Prediction of Biological Activity and Rule Extraction for HIV-1 Protease Inhibitors
— A fuzzy neural network (FNN) and multiple linear regression (MLR) were used to predict biological activities of 26 newly designed HIV-1 protease potential inhibitory compounds....
Razvan Andonie, Levente Fabry-Asztalos, Catharine ...
AAAI
2004
13 years 9 months ago
Fibring Neural Networks
Neural-symbolic systems are hybrid systems that integrate symbolic logic and neural networks. The goal of neural-symbolic integration is to benefit from the combination of feature...
Artur S. d'Avila Garcez, Dov M. Gabbay