A neural network model with bounded-weights for pattern classification

Authors:
Yi Liao;Shu-Cherng Fang;Henry L. W. Nuttle
Affiliations:
Operations Research and Industrial Engineering, North Carolina State University, Raleigh, NC;Operations Research and Industrial Engineering, North Carolina State University, Raleigh, NC;Operations Research and Industrial Engineering, North Carolina State University, Raleigh, NC
Venue:
Computers and Operations Research
Year:
2004

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Abstract

A new neural network model is proposed based on the concepts of multi-layer perceptrons, radial basis functions, and support vector machines (SVM). This neural network model is trained using the least squared error as the optimization criterion, with the magnitudes of the weights on the links being limited to a certain range. Like the SVM model, the weight specification problem is formulated as a convex quadratic programming problem. However, unlike the SVM model, it does not require that kernel functions satisfy Mercer's condition, and it can be readily extended to multi-class classification. Some experimental results are reported.