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08 February 2025
 
  » arxiv » 2301.01458

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An improved hybrid regularization approach for extreme learning machine
Liangjuan Zhou ; Wei Miao ;
Date 4 Jan 2023
AbstractExtreme 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
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