The Fast Computation Methods for Extreme Learning Machine

DOI

10.2991/icicci-15.2015.13How to use a DOI?

Abstract

Abstract—The extreme learning machine (ELM) that is proposed by Huang is designed based on single-hidden layer feedforward neural networks (SLFNs), which can randomly choose the parameters of hidden nodes and the output weights gotten analytically. So it can get the solution fastly. However, the learning time of ELM is mainly spent on calculating the Moore-Penrose generalized inverse matrices of the hidden layer output matrix. This paper mainly focuses on the effective computation of the Moore-Penrose generalized inverse matrices for ELM. Moreover, several methods are proposed, which are tensor product matrix ELM (TPM-ELM), Geninv ELM Numerical experiments show that both Geninv-ELM and TPM-ELM are faster than other kinds of ELM and can reach comparable generalization performance.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Tao Dou
AU  - Xu Zhou
PY  - 2015/09
DA  - 2015/09
TI  - The Fast Computation Methods for Extreme Learning Machine
BT  - Proceedings of the 2nd International Conference on Intelligent Computing and Cognitive Informatics
PB  - Atlantis Press
SP  - 55
EP  - 61
SN  - 1951-6851
UR  - https://doi.org/10.2991/icicci-15.2015.13
DO  - 10.2991/icicci-15.2015.13
ID  - Dou2015/09
ER  -