Enhanced extreme learning machine with modified gram-schmidt algorithm

Authors:
Jianchuan Yin;Nini Wang
Affiliations:
College of Navigation, Dalian Maritime University, Dalian, China;Department of Mathematics, Dalian Maritime University, Dalian, China
Venue:
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part I
Year:
2010

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Abstract

Extreme learning machine (ELM) has shown to be extremely fast with better generalization performance However, the implementation of ELM encounters two problems First, ELM tends to require more hidden nodes than conventional tuning-based algorithms Second, subjectivity is involved in choosing hidden nodes number In this paper, we apply the modified Gram-Schmidt (MGS) method to select hidden nodes which maximize the increment to explained variance of the desired output The Akaike's final prediction error (FPE) criterion are used to automatically determine the number of hidden nodes In comparison with conventional ELM learning method on several commonly used regressor benchmark problems, our proposed algorithm can achieve compact network with much faster response and satisfactory accuracy.