Discovering Efficient Learning Rules for Feedforward Neural Networks Using Genetic Programming

2003 ◽  
pp. 133-159 ◽  
Author(s):  
Amr Radi ◽  
Riccardo Poli
Keyword(s):  
Neural Networks ◽  
Genetic Programming ◽  
Feedforward Neural Networks ◽  
Learning Rules ◽  
Efficient Learning

Emergence of Learning Rule in Neural Networks Using Genetic Programming Combined with Decision Trees

1999 ◽  
Vol 3 (4) ◽  
pp. 223-233 ◽  
Author(s):  
Noboru Matsumoto ◽  
◽  
Kenneth J. Mackin ◽  
Eiichiro Tazaki
Keyword(s):  
Neural Networks ◽  
Decision Tree ◽  
Genetic Programming ◽  
Decision Trees ◽  
Back Propagation ◽  
Learning Rule ◽  
Divide And Conquer ◽  
Effective Learning ◽  
Learning Rules ◽  
Initial Network

Genetic Programming (GP) combined with Decision Trees is used to evolve the structure and weights for Artificial Neural Networks (ANN). The learning rule of the decision tree is defined as a function of global information using a divide-and-conquer strategy. Learning rules with lower fitness values are replaced by new ones generated by GP techniques. The reciprocal connection between decision tree and GP emerges from the coordination of learning rules. Since there is no constraint on initial network, a more suitable network is found for a given task. Fitness values are improved using a Hybrid GP technique combining GP and Back Propagation. The proposed method is applied to medical diagnosis and results demonstrate that effective learning rules evolve.

scholarly journals Asymptotic Prediction Error Variance for Feedforward Neural Networks

2020 ◽  
Vol 53 (2) ◽  
pp. 1108-1113
Author(s):  
Magnus Malmström ◽  
Isaac Skog ◽  
Daniel Axehill ◽  
Fredrik Gustafsson
Keyword(s):  
Neural Networks ◽  
Prediction Error ◽  
Feedforward Neural Networks ◽  
Error Variance ◽  
Prediction Error Variance
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