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论文类型：期刊论文

发表时间：2009-08-01

发表刊物：NEUROCOMPUTING

收录刊物：EI、SCIE、Scopus

卷号：72

期号：13-15

页面范围：3066-3076

ISSN号：0925-2312

关键字：Extreme learning machine; Lanczos bidiagonalization; Singular value decomposition; Regularization; Generalized cross validation

摘要：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.