Multilayer feedforward networks are universal approximators
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Summary
It is rigorously established that standard multilayer feedforward networks with as few as one hidden layer using arbitrary squashing functions are capable of approximating any Borel measurable function from one finite dimensional space to another to any desired degree of accuracy, provided sufficiently many hidden units are available.
- Type
- article
- Published
- 1989-07-01
- Cited by
- 24,597
- References
- 25
- OpenAlex
- https://openalex.org/W2137983211
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:2757547
Keywords
Feed forward, Feedforward neural network, Computer science, Class (philosophy), Function (biology)
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- Probability and Measure
- Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
- Multilayer feedforward networks are universal approximators
- Probability and Measure.
- Probability and Measure.
- Kolmogorov''s Mapping Neural Network Existence Theorem
- On the Representation of Continuous Functions of Several Variables as Superpositions of Continuous Functions of one Variable and Addition
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