What is recorded here?
Proved that finite superpositions using a continuous sigmoidal nonlinearity can uniformly approximate continuous functions on the unit hypercube, establishing a representational result for single-hidden-layer feedforward networks.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
The theorem is a representational existence result under the paper's continuity, domain and activation assumptions. It does not guarantee efficient learning, finite-sample generalization, optimization success, minimal width or the performance of deeper architectures.
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People and systems
G. Cybenko
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Documented countries / regions: United States
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English catalogue narrative
Cybenko: approximation by superpositions of a sigmoidal function
Proved that finite superpositions using a continuous sigmoidal nonlinearity can uniformly approximate continuous functions on the unit hypercube, establishing a representational result for single-hidden-layer feedforward networks.
Scientific scope
The theorem is a representational existence result under the paper's continuity, domain and activation assumptions. It does not guarantee efficient learning, finite-sample generalization, optimization success, minimal width or the performance of deeper architectures.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
G. Cybenko
The named authors contributed to this scoped work; individual effort shares and later applications are not assigned by the byline.