Extreme learning machine: algorithm, theory and applications

By Shifei Ding, Han Zhao, Yanan Zhang, Xinzheng Xu, Ru Nie. Artificial Intelligence Review, 2013

556 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/80ab1a48aac6c2716cb52334ca2f0872adfe3a6b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Xiaobo Shen
2. Ivor Wai-Hung Tsang
3. Haijun Lv
4. Ning Chen
5. Haobo Wang
6. Weiwei Liu
7. Yongquan Yang
8. Mingsheng Long

In Electrical and Electronic Engineering

1. Ivor Wai-Hung Tsang
2. Weiwei Liu

Citations in the corpus

• The Emerging Trends of Multi-Label Learning (2020, methodology): “OSMLELM uses a sigmoid activation function and outputs weights to predict the labels.”
• Online Metric Learning for Multi-Label Classification (2020, unknown): “(Ding et al. 2015) developed a single-hidden layer feedforward neural network-based learning technique named ELM.”
• A Survey on ensemble learning under the era of deep learning (2021, methodology): “2020) proposed an ensemble learning algorithm based on wavelet transform (Daubechies and Bates 1993), extreme learning machine (ELM) (Ding et al. 2015; Huang et al. 2015) and partial least squares regression (PLSR) (Geladi and Kowalski 1986).”