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Use of some sensitivity criteria for choosing networks with good generalization ability

6/6 checkable references clean · checked 2026-08-29

Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a retraction, withdrawal, or removal notice.

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The 6 checked references that resolve

resolves10.1007/BF02551274

Approximation by superpositions of a sigmoidal function

resolves10.1007/978-3-642-79119-2_1

An Overview of Predictive Learning and Function Approximation

resolves10.1109/ICNN.1993.298559

Sensitivity analysis for input vector in multilayer feedforward neural networks

resolves10.1007/978-94-009-0643-3_70

An Approach to Generalization Problem in Back-Propagation Learning

resolves10.1007/978-94-009-0643-3_66

Curvature-Driven Smoothing in Backpropagation Neural Networks

resolves10.1109/72.286908

Functional approximation by feed-forward networks: a least-squares approach to generalization

The 1 reference without a DOI — listed, not checked

no DOI — not checkedA. S. Weigend, B. A. Huberman and D. E. Rumelhart. Predicting sunspots and exchange rates with connectionist networks, in M. Casdagli and S. Eubank eds.,Nonlinear Modeling and Forecasting, Addison-Wesley, pp. 395–432, 1992.

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