Batch gradient method with smoothing \(L_{1/2}\) regularization for training of feedforward neural networks

DOI10.1016/j.neunet.2013.11.006zbMath1298.68233OpenAlexW2088350412WikidataQ50706014 ScholiaQ50706014MaRDI QIDQ470178

Jacek M. Zurada, Wei Wu, Dakun Yang, Qinwei Fan, Yan Liu, Ji'an Wang

Publication date: 12 November 2014

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

Full work available at URL: https://doi.org/10.1016/j.neunet.2013.11.006

zbMATH Keywords

convergencefeedforward neural networksbatch gradient methodsmoothing \(L_{1/2}\) regularization

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Neural networks for/in biological studies, artificial life and related topics (92B20)

Related Items (7)

Deterministic convergence analysis via smoothing group Lasso regularization and adaptive momentum for Sigma-Pi-Sigma neural network ⋮ Convergence analysis of an augmented algorithm for fully complex-valued neural networks ⋮ The convergence analysis of spikeprop algorithm with smoothing \(L_{1/2}\) regularization ⋮ A New Smoothing Approach for Piecewise Smooth Functions: Application to Some Fundamental Functions ⋮ Convergence analysis for sigma-pi-sigma neural network based on some relaxed conditions ⋮ A backpropagation learning algorithm with graph regularization for feedforward neural networks ⋮ Modeling of complex dynamic systems using differential neural networks with the incorporation of a priori knowledge

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