Pattern recognition using neural networks: theory and algorithms for engineers and scientists
Pattern recognition using neural networks: theory and algorithms for engineers and scientists
Reduction Techniques for Instance-BasedLearning Algorithms
Machine Learning
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Evolving RBF neural networks for time-series forecasting with EvRBF
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Informatics and computer science intelligent systems applications
Computational Intelligence: Principles, Techniques and Applications
Computational Intelligence: Principles, Techniques and Applications
Improving multiclass pattern recognition with a co-evolutionary RBFNN
Pattern Recognition Letters
Automatic Neural Net Design by Means of a Symbiotic Co-evolutionary Algorithm
HAIS '08 Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems
KEEL: a software tool to assess evolutionary algorithms for data mining problems
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Evolutionary and Metaheuristics based Data Mining (EMBDM); Guest Editors: José A. Gámez, María J. del Jesús, José M. Puerta
A hybrid classification algorithm based on coevolutionary EBFNN and domain covering method
Neural Computing and Applications
Automatic design of hierarchical RBF networks for system identification
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
A cooperative coevolution algorithm of RBFNN for classification
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Evolving RBF neural networks for pattern classification
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
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Performance and accuracy of a neural network are strongly related to its design. Designing a neural network involves topology (number of neurons, number of layers, number of synapses between layers, etc.), training synapse weights, and parameter selection. Radial basis function neural networks (RBFNNs) could additionally require some other parameters, for example, the means and standard deviations if the activation function of neurons in the hidden layer is a Gaussian function. Commonly, Genetic Algorithms and Evolution Strategies have been used for automatically designing RBFNNs In this work, the use of prototype selection methods for designing a RBFNN is proposed and studied. Experimental results show the viability of designing RBFNNs using prototype selection.