Neuron selection for RBF neural network classifier based on data structure preserving criterion.
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.
Mao
K Z
KZ
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore. ekzmao@ntu.edu.sg
Huang
Guang-Bin
GB
D000465:Algorithms; D016000:Cluster Analysis; D003198:Computer Simulation; D016208:Databases, Factual; D016247:Information Storage and Retrieval / Q000379:methods*; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*