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» Optimizing number of hidden neurons in neural networks
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FLAIRS
2006
13 years 9 months ago
GFAM: Evolving Fuzzy ARTMAP Neural Networks
Fuzzy ARTMAP (FAM) is one of the best neural network architectures in solving classification problems. One of the limitations of Fuzzy ARTMAP that has been extensively reported in...
Ahmad Al-Daraiseh, Michael Georgiopoulos, Annie S....
GECCO
2004
Springer
116views Optimization» more  GECCO 2004»
14 years 1 months ago
Reducing Fitness Evaluations Using Clustering Techniques and Neural Network Ensembles
Abstract. In many real-world applications of evolutionary computation, it is essential to reduce the number of fitness evaluations. To this end, computationally efficient models c...
Yaochu Jin, Bernhard Sendhoff
EVOW
2008
Springer
13 years 9 months ago
Architecture Performance Prediction Using Evolutionary Artificial Neural Networks
The design of computer architectures requires the setting of multiple parameters on which the final performance depends. The number of possible combinations make an extremely huge ...
Pedro A. Castillo, Antonio Miguel Mora, Juan Juli&...
27
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NIPS
1990
13 years 9 months ago
Convergence of a Neural Network Classifier
In this paper, we show that the LVQ learning algorithm converges to locally asymptotic stable equilibria of an ordinary differential equation. We show that the learning algorithm ...
John S. Baras, Anthony LaVigna
EVOW
2007
Springer
14 years 2 months ago
An Adaptive Global-Local Memetic Algorithm to Discover Resources in P2P Networks
This paper proposes a neural network based approach for solving the resource discovery problem in Peer to Peer (P2P) networks and an Adaptive Global Local Memetic Algorithm (AGLMA)...
Ferrante Neri, Niko Kotilainen, Mikko Vapa