Constructive algorithms for structure learning in feedforward neural networks for regression problems

1997/05/01 by Tin-Yau Kwok, Dit-Yan Yeung · 1 citation
Computer Science · Engineering · #Blind Source Separation Techniques #Fault Detection and Control Systems #Neural Networks and Applications

paper · doi:10.1109/72.572102

openalex publication_date 1997/05/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/10

Abstract

In this survey paper, we review the constructive algorithms for structure learning in feedforward neural networks for regression problems. The basic idea is to start with a small network, then add hidden units and weights incrementally until a satisfactory solution is found. By formulating the whole problem as a state-space search, we first describe the general issues in constructive algorithms, with special emphasis on the search strategy. A taxonomy, based on the differences in the state transition mapping, the training algorithm, and the network architecture, is then presented.

Citations

• Generalized Cross-Validation as a Method for Choosing a Good Ridge Parameter
• A review of Bayesian neural networks with an application to near infrared spectroscopy
• Neural Networks and the Bias/Variance Dilemma
• A Practical Bayesian Framework for Backpropagation Networks
• Smoothing noisy data with spline functions

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