Hierarchical Extreme Learning Machines
Abstract
The techniques and theories of the Extreme Learning Machines (ELM) have been developing fast with the contributions from researchers over the world in the past 10 years. ELM originally aims to fill the gap between machine learning and biological learning. ELM as a common learning mechanism may play important roles in both machine learning and biological learning irrespective of whether they are implemented in silicon, proteins or other materials. ELM theories state that as long as neurons are nonlinear piecewise continuous (even without knowing their mathematical modeling), they can be randomly generated in both artificial and biological neural networks. Random neurons may play important roles in biological learning mechanism, that is, biological learning systems are globally structured but may be locally disordered.
Recommended Citation
G. Huang et al., "Hierarchical Extreme Learning Machines," Neurocomputing, vol. 277, Elsevier, Feb 2018.
The definitive version is available at https://doi.org/10.1016/j.neucom.2017.07.067
Department(s)
Electrical and Computer Engineering
Research Center/Lab(s)
Intelligent Systems Center
Second Research Center/Lab
Center for High Performance Computing Research
International Standard Serial Number (ISSN)
0925-2312
Document Type
Editorial
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2018 Elsevier, All rights reserved.
Publication Date
01 Feb 2018
