Batch Gradient Learning Algorithm with Smoothing L1 Regularization for Feedforward Neural Networks

Regularization techniques are critical in the development of machine learning models. Complex models, such as neural networks, are particularly prone to overfitting and to performing poorly on the training data. L1 regularization is the most extreme way to enforce sparsity, but, regrettably, it does...

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Veröffentlicht in:	Computers (Basel) Jg. 12; H. 1; S. 4
1. Verfasser:	Mohamed, Khidir Shaib
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 01.01.2023
Schlagworte:	Algorithms Artificial neural networks Brain Convergence Error functions Machine learning Neural networks Neurons Optimization Regularization Regularization methods Smoothing Sparsity
ISSN:	2073-431X, 2073-431X
Online-Zugang:	Volltext
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