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Article overview
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An improved hybrid regularization approach for extreme learning machine | Liangjuan Zhou
; Wei Miao
; | Date: |
4 Jan 2023 | Abstract: | Extreme learning machine (ELM) is a network model that arbitrarily
initializes the first hidden layer and can be computed speedily. In order to
improve the classification performance of ELM, a $ell_2$ and $ell_{0.5}$
regularization ELM model ($ell_{2}$-$ell_{0.5}$-ELM) is proposed in this
paper. An iterative optimization algorithm of the fixed point contraction
mapping is applied to solve the $ell_{2}$-$ell_{0.5}$-ELM model. The
convergence and sparsity of the proposed method are discussed and analyzed
under reasonable assumptions. The performance of the proposed
$ell_{2}$-$ell_{0.5}$-ELM method is compared with BP, SVM, ELM,
$ell_{0.5}$-ELM, $ell_{1}$-ELM, $ell_{2}$-ELM and $ell_{2}$-$ell_{1}$ELM,
the results show that the prediction accuracy, sparsity, and stability of the
$ell_{2}$-$ell_{0.5}$-ELM are better than the other $7$ models. | Source: | arXiv, 2301.01458 | Services: | Forum | Review | PDF | Favorites |
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