# Multilayer feedforward networks with a nonpolynomial activation function can approximate any function

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/multilayer-feedforward-networks-with-a-nonpolynomial-activation-function-can/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Moshe Leshno,Valdimir Ya. Lin,Allan Pinkus,Shimon Schocken |
| Citations | 288 |
| DOI | 10.1016/s0893-6080(05)80131-5 |
| Fields | Computer Science |
| Open Access | true |
| OA Status | green |
| OA URL | http://archive.nyu.edu/bitstream/2451/14329/1/IS-92-13.pdf |
| OpenAlex ID | https://openalex.org/W2043005456 |
| Type | article |
| Year | 1993 |

## Paper authors

- [Moshe Leshno](https://scholariq.org/researchers/moshe-leshno/)

## Paper journal

- [Neural Networks](https://scholariq.org/journals/neural-networks/)

## Paper primary topic

- [Neural Networks and Applications](https://scholariq.org/topics/neural-networks-and-applications/)

## Paper topics

- [Neural Networks and Applications](https://scholariq.org/topics/neural-networks-and-applications/)
- [Face and Expression Recognition](https://scholariq.org/topics/face-and-expression-recognition/)
- [Machine Learning and ELM](https://scholariq.org/topics/machine-learning-and-elm/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.