Convergence of batch gradient algorithm with smoothing composition of group l0 and l1/2 regularization for feedforward neural networks

In this paper, we prove the convergence of batch gradient method for training feedforward neural network; we have proposed a new penalty term based on composition of smoothing L 1 / 2 penalty for weights vectors incoming to hidden nodes and smoothing group L 0 regularization for the resulting vector...

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Vydáno v:	Progress in artificial intelligence Ročník 11; číslo 3; s. 269 - 278
Hlavní autoři:	Ramchoun, Hassan, Ettaouil, Mohamed
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2022 Springer Nature B.V
Témata:	Algorithms Artificial Intelligence Artificial neural networks Composition Computational Intelligence Computer Imaging Computer Science Control Convergence Data Mining and Knowledge Discovery Mechatronics Natural Language Processing (NLP) Nodes Pattern Recognition and Graphics Regular Paper Regularization Robotics Smoothing Training Vision Multilayer perceptron Smoothing group Smoothing regularization Convergence
ISSN:	2192-6352, 2192-6360
On-line přístup:	Získat plný text
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