Journal
NEUROCOMPUTING
Volume 72, Issue 13-15, Pages 3066-3076Publisher
ELSEVIER
DOI: 10.1016/j.neucom.2009.03.016
Keywords
Extreme learning machine; Lanczos bidiagonalization; Singular value decomposition; Regularization; Generalized cross validation
Categories
Funding
- National Nature Science Foundation of China [60674073]
- National Major Technology R&D Program of China [2006BAB14B05]
- National Basic Research Program of China [2006CB403405]
- National High Technology Research and Development Program of P.R. China [2007AA04Z158]
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available