ONLINE SEQUENTIAL LEARNING BASED ON ENHANCED EXTREME LEARNING MACHINE USING LEFT OR RIGHT PSEUDO-INVERSE

Weiwei Zong, Yuan Lan, Guang-Bin Huang

Abstract

The latest development (Huang et al., 2011) has shown that better generalization performance can be obtained for extreme learning machine (ELM) by adding a positive value to the diagonal of HTH or HHT , where H is the hidden layer output matrix. This paper further extends this enhanced ELM to online sequential learning mode. An online sequential learning algorithm is proposed for SLFNs and other regularization networks, consisting of two formulas for two kinds of scenarios: when initial training data is of small scale or large scale. Performance of proposed online sequential learning algorithm is demonstrated through six benchmarking data sets for both regression and multi-class classification problems.

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Paper Citation


in Harvard Style

Zong W., Lan Y. and Huang G. (2012). ONLINE SEQUENTIAL LEARNING BASED ON ENHANCED EXTREME LEARNING MACHINE USING LEFT OR RIGHT PSEUDO-INVERSE . In Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-8425-98-0, pages 300-305. DOI: 10.5220/0003777603000305


in Bibtex Style

@conference{icpram12,
author={Weiwei Zong and Yuan Lan and Guang-Bin Huang},
title={ONLINE SEQUENTIAL LEARNING BASED ON ENHANCED EXTREME LEARNING MACHINE USING LEFT OR RIGHT PSEUDO-INVERSE},
booktitle={Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2012},
pages={300-305},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003777603000305},
isbn={978-989-8425-98-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - ONLINE SEQUENTIAL LEARNING BASED ON ENHANCED EXTREME LEARNING MACHINE USING LEFT OR RIGHT PSEUDO-INVERSE
SN - 978-989-8425-98-0
AU - Zong W.
AU - Lan Y.
AU - Huang G.
PY - 2012
SP - 300
EP - 305
DO - 10.5220/0003777603000305