# Machine Learning and ELM

**Type:** Topics  
**Canonical URL:** https://scholariq.org/topics/machine-learning-and-elm-2/

## Facts

| Field | Value |
| --- | --- |
| Citations | 268,129 |
| Description | This cluster of papers focuses on the theory, applications, and advancements in Extreme Learning Machines (ELM), a machine learning framework based on feedforward neural networks with random hidden nodes. The papers cover topics such as incremental learning, classification, regression, ensemble methods, kernel-based models, and their applications in various domains. |
| Domain | Physical Sciences |
| Field | Computer Science |
| OpenAlex ID | https://openalex.org/T12676 |
| Works | 17,101 |

## Topic researchers

Showing 12 of 20.

- [Yoshua Bengio](https://scholariq.org/researchers/yoshua-bengio/)
- [Andrew Zisserman](https://scholariq.org/researchers/andrew-zisserman/)
- [Yann LeCun](https://scholariq.org/researchers/yann-lecun/)
- [Vladimir Vapnik](https://scholariq.org/researchers/vladimir-vapnik/)
- [Trevor Darrell](https://scholariq.org/researchers/trevor-darrell/)
- [Michael I. Jordan](https://scholariq.org/researchers/michael-i-jordan/)
- [Christian Szegedy](https://scholariq.org/researchers/christian-szegedy/)
- [David Silver](https://scholariq.org/researchers/david-silver/)
- [Anil K. Jain](https://scholariq.org/researchers/anil-k-jain/)
- [Seyedali Mirjalili](https://scholariq.org/researchers/seyedali-mirjalili/)
- [Bernhard Schölkopf](https://scholariq.org/researchers/bernhard-scholkopf/)
- [Huan Liu](https://scholariq.org/researchers/huan-liu/)

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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.