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Indexed by:期刊论文

Date of Publication:2009-08-01

Journal:NEUROCOMPUTING

Included Journals:EI、SCIE、Scopus

Volume:72

Issue:13-15

Page Number:3066-3076

ISSN No.:0925-2312

Key Words:Extreme learning machine; Lanczos bidiagonalization; Singular value decomposition; Regularization; Generalized cross validation

Abstract:There are two problems preventing the further development of extreme learning machine (ELM). First, the ill-conditioning of hidden layer output matrix reduces the stability of ELM. Second, the complexity of singular value decomposition (SVD) for computing Moore-Penrose generalized inverse limits the learning speed of ELM. For these two problems, this paper proposes the partial Lanczos ELM (PL-ELM) which employs the hybrid of partial Lanczos bidiagonalization and SVD to compute output weights. Experimental results indicate that, compared with ELM, PL-ELM not only effectively improves the stability and generalization performance but also raises the learning speed. (C) 2009 Elsevier B.V. All rights reserved.