Local Receptive Fields Based Extreme Learning Machine

By G. Huang, Zuo Bai, Liyanaarachchi Lekamalage Chamara Kasun, Chi-Man Vong. IEEE Computational Intelligence Magazine, 2015

331 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/912597e993e8c42c7535751a09638c834b9af94e

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. Mark D. McDonnell
2. Abdulhameed Alelaiwi
3. Tony Vladusich
4. Mohammad Mehedi Hassan
5. Giancarlo Fortino
6. Abdu H. Gumaei
7. Md. Rafiul Hassan
8. G. Huang

In Electrical and Electronic Engineering

1. Abdulhameed Alelaiwi
2. Mark D. McDonnell
3. Tony Vladusich
4. Giancarlo Fortino
5. Mohammad Mehedi Hassan
6. Abdu H. Gumaei
7. Md. Rafiul Hassan
8. G. Huang

Citations in the corpus

• A Hybrid Feature Extraction Method With Regularized Extreme Learning Machine for Brain Tumor Classification (2019, background): “Even though deep learning methods improve the classification of brain tumors, they need a large amount of training samples and a high cost of computation, and they take a long time for training [16].”
• What are Extreme Learning Machines? Filling the Gap Between Frank Rosenblatt’s Dream and John von Neumann’s Puzzle (2015, methodology): “ELM targets at not only ‘‘generalized’’ single-hiddenlayer feedforward networks but also ‘‘generalized’’ multihidden-layer feedforward networks in which a node may be a subnetwork consisting of other hidden nodes [5, 8, 26].”
• Enhanced image classification with a fast-learning shallow convolutional neural network (2015, methodology): “Third, we downsample following pooling, whereas [35] does not do so.”