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Cholesky factorization based online regularized and kernelized extreme learning machines with forgetting mechanism.
- Source :
-
Neurocomputing . Jan2016 Part B, Vol. 174, p1147-1155. 9p. - Publication Year :
- 2016
-
Abstract
- In this paper, we propose two alternative schemes of fast online sequential extreme learning machine (ELM) for training the single hidden-layer feedforward neural networks (SLFN), termed as Cholesky factorization based online regularized ELM with forgetting mechanism (CF-FORELM) and Cholesky factorization based online kernelized ELM with forgetting mechanism (CF-FOKELM). First, the solutions of regularized ELM (RELM) and kernelized ELM (KELM) using the matrix Cholesky factorization are introduced; then the recursive method for calculating Cholesky factor of involved matrix in RELM and KELM is designed when RELM and KELM are applied to train SLFN online; consequently, the CF-FORELM and CF-FOKELM are obtained. The numerical simulation results show CF-FORELM demands less computational burden than Dynamic Regression ELM (DR-ELM), and CF-FOKELM also owns higher computational efficiency than both FOKELM and online sequential ELM with kernels (OS-ELMK), and CF-FORELM is less sensitive to model parameters than CF-FOKELM. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09252312
- Volume :
- 174
- Database :
- Academic Search Index
- Journal :
- Neurocomputing
- Publication Type :
- Academic Journal
- Accession number :
- 111320722
- Full Text :
- https://doi.org/10.1016/j.neucom.2015.10.033