# A study on effectiveness of extreme learning machine

arXiv: https://arxiv.org/abs/1409.3924 · HTML: https://mlsanity.com/paper/1409.3924/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Sep 2014.

## Built on by (most cited first, 4 of 4)

- [A General Framework for Hypercomplex-valued Extreme Learning Machines](https://mlsanity.com/paper/2101.06166/) (2021)
- [Numerical Approximation Capacity of Neural Networks with Bounded Parameters: Do Limits Exist, and How Can They Be Measured?](https://mlsanity.com/paper/2409.16697/) (2024)
- [On the accuracy of interpolation based on single-layer artificial neural networks with a focus on defeating the Runge phenomenon](https://mlsanity.com/paper/2308.10720/) (2023)
- [Effective Non-Random Extreme Learning Machine](https://mlsanity.com/paper/2411.16229/) (2024)

## Abstract (arXiv)

Extreme learning machine (ELM), proposed by Huang et al., has been shown a promising learning algorithm for single-hidden layer feedforward neural networks (SLFNs). Nevertheless, because of the random choice of input weights and biases, the ELM algorithm sometimes makes the hidden layer output matrix H of SLFN not full column rank, which lowers the effectiveness of ELM. This paper discusses the effectiveness of ELM and proposes an improved algorithm called EELM that makes a proper selection of the input weights and bias before calculating the output weights, which ensures the full column rank of H in theory. This improves to some extend the learning rate (testing accuracy, prediction accuracy, learning time) and the robustness property of the networks. The experimental results based on both the benchmark function approximation and real-world problems including classification and regression applications show the good performances of EELM.