Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Three learning phases for radial-basis-function networks
Neural Networks
On different facets of regularization theory
Neural Computation
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume IV-Volume 7472 - Volume 7472
A Theory of Networks for Approximation and Learning
A Theory of Networks for Approximation and Learning
Stochastic simulations of web search engines: RBF versus second-order regression models
Information Sciences—Informatics and Computer Science: An International Journal
Feature selection, L1 vs. L2 regularization, and rotational invariance
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Algebraic Analysis for Nonidentifiable Learning Machines
Neural Computation
A Modified Generalized RBF Model with EM-based Learning Algorithm for Medical Applications
CBMS '06 Proceedings of the 19th IEEE Symposium on Computer-Based Medical Systems
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Classifier learning with a new locality regularization method
Pattern Recognition
Fast learning in networks of locally-tuned processing units
Neural Computation
Diversity of ability and cognitive style for group decision processes
Information Sciences: an International Journal
Information Sciences: an International Journal
Decision trees can initialize radial-basis function networks
IEEE Transactions on Neural Networks
Model complexity control for regression using VC generalization bounds
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
So near and yet so far: New insight into properties of some well-known classifier paradigms
Information Sciences: an International Journal
Nonlinear mappings in problem solving and their PSO-based development
Information Sciences: an International Journal
Dynamic fusion method using Localized Generalization Error Model
Information Sciences: an International Journal
MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Computational Intelligence - Volume Part II
Information Sciences: an International Journal
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Training a classifier with good generalization capability is a major issue for pattern classification problems. A novel training objective function for Radial Basis Function (RBF) network using a localized generalization error model (L-GEM) is proposed in this paper. The localized generalization error model provides a generalization error bound for unseen samples located within a neighborhood that contains all training samples. The assumption of the same width for all dimensions of a hidden neuron in L-GEM is relaxed in this work. The parameters of RBF network are selected via minimization of the proposed objective function to minimize its localized generalization error bound. The characteristics of the proposed objective function are compared with those for regularization methods. For weight selection, RBF networks trained by minimizing the proposed objective function consistently outperform RBF networks trained by minimizing the training error, Tikhonov Regularization, Weight Decay or Locality Regularization. The proposed objective function is also applied to select center, width and weight in RBF network simultaneously. RBF networks trained by minimizing the proposed objective function yield better testing accuracies when compared to those that minimizes training error only.