Increasing classification efficiency with multiple mirror classifiers
Expert Systems with Applications: An International Journal
Optimum Neural Network Construction Via Linear Programming Minimum Sphere Set Covering
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
An Enhanced Swarm Intelligence Clustering-Based RBF Neural Network Detection Classifier
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
An Enhanced Swarm Intelligence Clustering-Based RBF Neural Network Web Text Classifier
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
IEEE Transactions on Neural Networks
LSMS'07 Proceedings of the Life system modeling and simulation 2007 international conference on Bio-Inspired computational intelligence and applications
A fast multi-output RBF neural network construction method
Neurocomputing
Two-stage extreme learning machine for regression
Neurocomputing
Frontiers of Computer Science in China
Construction cosine radial basic function neural networks based on artificial immune networks
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications - Volume Part II
Neuron selection for RBF neural network classifier based on multiple granularities immune network
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
An adaptive classifier based on artificial immune network
LSMS'07 Proceedings of the 2007 international conference on Life System Modeling and Simulation
Privacy preserving neural networks in iris signature feature extraction
Proceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environments
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The central problem in training a radial basis function neural network is the selection of hidden layer neurons. In this paper, we propose to select hidden layer neurons based on data structure preserving criterion. Data structure denotes relative location of samples in the high-dimensional space. By preserving the data structure of samples including those that are close to separation boundaries between different classes, the neuron subset selected retains the separation margin underlying the full set of hidden layer neurons. As a direct result, the network obtained tends to generalize well.