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APIN
2010
107views more  APIN 2010»
13 years 4 months ago
Extracting reduced logic programs from artificial neural networks
Artificial neural networks can be trained to perform excellently in many application areas. While they can learn from raw data to solve sophisticated recognition and analysis prob...
Jens Lehmann, Sebastian Bader, Pascal Hitzler
ASC
2004
13 years 4 months ago
Extracting rules from trained neural network using GA for managing E-business
Theabilitytointelligentlycollect,manageandanalyzeinformationaboutcustomersandsellersisakeysourceofcompetitive advantage for an e-business. This ability provides an opportunity to ...
Atta Ebrahim E. ElAlfi, R. Haque, M. Esmel ElAlami
APIN
1999
110views more  APIN 1999»
13 years 4 months ago
The Connectionist Inductive Learning and Logic Programming System
The Connectionist Inductive Learning and Logic Programming System, C-IL 2 P, integrates the symbolic and connectionist paradigms of Artificial Intelligence through neural networks...
Artur S. d'Avila Garcez, Gerson Zaverucha
ICTAI
1996
IEEE
13 years 9 months ago
Forward-Tracking: A Technique for Searching Beyond Failure
In many applications, such as decision support, negotiation, planning, scheduling, etc., one needs to express requirements that can only be partially satisfied. In order to expres...
Elena Marchiori, Massimo Marchiori, Joost N. Kok
IJCNN
2008
IEEE
13 years 11 months ago
Evolving a neural network using dyadic connections
—Since machine learning has become a tool to make more efficient design of sophisticated systems, we present in this paper a novel methodology to create powerful neural network ...
Andreas Huemer, Mario A. Góngora, David A. ...