universal approximation theorem
GPTKB entity
Statements (19)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
gptkb:mathematical_concept
|
| gptkbp:appliesTo |
feedforward neural networks
|
| gptkbp:describes |
capability of neural networks to approximate functions
|
| gptkbp:doesNotGuarantee |
generalization
efficient learning practical network size |
| gptkbp:field |
gptkb:machine_learning
gptkb:mathematics neural networks |
| gptkbp:generalizes |
deep learning theory
|
| https://www.w3.org/2000/01/rdf-schema#label |
universal approximation theorem
|
| gptkbp:provenBy |
gptkb:George_Cybenko
|
| gptkbp:relatedTo |
function approximation
activation function |
| gptkbp:requires |
sufficiently large hidden layer
|
| gptkbp:state |
a feedforward network with a single hidden layer can approximate any continuous function under mild assumptions
|
| gptkbp:yearProved |
1989
|
| gptkbp:bfsParent |
gptkb:no_free_lunch_theorem
|
| gptkbp:bfsLayer |
5
|