Regularized Extreme Learning Machine

By W. Deng, Qinghua Zheng, Lin Chen. IEEE Symposium on Computational Intelligence and Data Mining, 2009

461 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cancer Research

View Paper: https://www.semanticscholar.org/paper/e4de0f69cd867dbcae88211ac05318be17615a66

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. G. Huang
2. Erik Cambria
3. Yuan Lan
4. Dianhui Wang
5. Hongming Zhou
6. Xiaojian Ding
7. Rui Zhang
8. Iti Chaturvedi

In Cancer Research

1. G. Huang
2. Yiqiang Chen
3. Weiwei Zong

Citations in the corpus

• Extreme Learning Machine for Regression and Multiclass Classification (2012, methodology): “Toh [22] and Deng et al. [21] have studied the performance of ELM with this enhancement under the Sigmoid additive type of SLFNs.”
• Weighted extreme learning machine for imbalance learning (2013, methodology): “[10], though none is targeting the imbalanced data problem.”
• Bayesian network based extreme learning machine for subjectivity detection (2017, methodology): “This is the peculiar aspect of ELM: the hidden weights are set randomly and can be tuned using a regularized least squares (RLS) problem in a linear space [26].”
• Extreme learning machines: a survey (2011, background): “[42] studied such regularization enhancement under sigmoid additive type of SLFNs.”