An approach to generate rules from neural networks for regression problems.

DOI10.1016/S0377-2217(02)00792-0zbMath1045.62073OpenAlexW1969097421MaRDI QIDQ1428066

James Y. L. Thong, Rudy Setiono

Publication date: 14 March 2004

Published in: European Journal of Operational Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0377-2217(02)00792-0

zbMATH Keywords

Neural networksNonlinear regressionMachine learningKnowledge-based systemsCurve fitting

Mathematics Subject Classification ID

Linear inference, regression (62J99) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (3)

Lyapunov stability-dynamic back propagation-based comparative study of different types of functional link neural networks for the identification of nonlinear systems ⋮ Building an interpretable fuzzy rule base from data using Orthogonal Least Squares - Application to a depollution problem ⋮ Unnamed Item

Uses Software

• UCI-ml
• C4.5

Cites Work

• Unnamed Item
• A Penalty-Function Approach for Pruning Feedforward Neural Networks
• Managerial Applications of Neural Networks: The Case of Bank Failure Predictions
• Approximation by superpositions of a sigmoidal function

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