Feb 7, 2014 · In this paper we combine the L 1/2 regularization method with extreme learning machine to prune extreme learning machine. A variable learning ...

In this paper we combine the L 1/2 regularization method with extreme learning machine to prune extreme learning machine. A variable learning coefficient is ...

In this paper we combine the L1/2 regularization method with extreme learning machine to prune extreme learning machine. A variable learning coefficient is ...

In this paper, a novel pruning algorithm based on sensitivity analysis is proposed for ELM. The measure to estimate the necessary number of hidden layer nodes ...

Ye-tian Fan, Wei Wu, Wenyu Yang, Qin-wei Fan , Jian Wang : A pruning algorithm with L 1/2 regularizer for extreme learning machine. J. Zhejiang Univ. Sci.

In this paper we combine the L1/2 regularization method with extreme learning machine to prune extreme learning machine. A variable learning coefficient is ...

In this paper, a pruning ensemble model of ELM with \(L_{1/2} \) regularizer (PE-ELMR) is proposed to solve above problems. It involves two stages. First, we ...

In this paper, a pruning ensemble model of ELM with L1/2 regularizer (PE-ELMR) is proposed to solve above problems. It involves two stages. First, we replace ...

In this paper, a pruning ensemble model of ELM with L 1 / 2 L1/2 regularizer (PE-ELMR) is proposed to solve above problems.

A pruning extreme learning machine with $$L_{2, 1/2}$$ regularization for multi-dimensional output problems. https://doi.org/10.1007/s13042-023-01929-z.