The generalized extreme learning machines: tuning hyperparameters and limiting approach for the Moore-Penrose generalized inverse

DOI10.1016/j.neunet.2021.09.008zbMath1521.68187OpenAlexW3199979266MaRDI QIDQ6055151

Meejoung Kim

Publication date: 28 September 2023

Published in: Neural Networks (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.neunet.2021.09.008

zbMATH Keywords

Moore-Penrose generalized inverseuniversal approximationgeneralized extreme learning machineattack predictionmultiple hidden-layer feedforward neural networksoutput weight matrix

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Theory of matrix inversion and generalized inverses (15A09)

Cites Work

• Two-hidden-layer feed-forward networks are universal approximators: a constructive approach
• On the approximation by single hidden layer feedforward neural networks with fixed weights
• Regression and the Moore-Penrose pseudoinverse
• Theoretical issues in deep networks
• Unnamed Item
• Unnamed Item
• Unnamed Item