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Cited by 3,252

The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.

Networks for approximation and learning

Poggio, T., Girosi, F.·1990·Proceedings of the IEEE
DOI 10.1109/5.58326
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Abstract

The problem of the approximation of nonlinear mapping, (especially continuous mappings) is considered. Regularization theory and a theoretical framework for approximation (based on regularization techniques) that leads to a class of three-layer networks called regularization networks are discussed. Regularization networks are mathematically related to the radial basis functions, mainly used for strict interpolation tasks. Learning as approximation and learning as hypersurface reconstruction are discussed. Two extensions of the regularization approach are presented, along with the approach's corrections to splines, regularization, Bayes formulation, and clustering. The theory of regularization networks is generalized to a formulation that includes task-dependent clustering and dimensionality reduction. Applications of regularization networks are discussed.>

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Metadata

Authors
Poggio, T., & Girosi, F.
Venue
Proceedings of the IEEE
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
1990
Volume
78
Issue
9
Pages
1481-1497
ISSN
0018-9219 (ISSN-L)
DOI
10.1109/5.58326
OpenAlex
W2143956139
Cited by
3,252
References
9
FWCI
66.08
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Networks for approximation and learning. https://app.cochat.ai/papers/10.1109/5.58326
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Poggio, T., & Girosi, F. (1990). Networks for approximation and learning. Proceedings of the IEEE. https://doi.org/10.1109/5.58326

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

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