KAF + RSigELU: a nonlinear and kernel-based activation function for deep neural networks

Activation functions (AFs) are the basis for neural network architectures used in real-world problems to accurately model and learn complex relationships between variables. They are preferred to process the input information coming to the network and to produce the corresponding output. The kernel-b...

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Vydáno v:	Neural computing & applications Ročník 34; číslo 16; s. 13909 - 13923
Hlavní autoři:	Kiliçarslan, Serhat, Celik, Mete
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London Springer London 01.08.2022 Springer Nature B.V
Témata:	Artificial Intelligence Artificial neural networks Complex variables Computational Biology/Bioinformatics Computational Science and Engineering Computer architecture Computer Science Data Mining and Knowledge Discovery Image Processing and Computer Vision Kernel functions Machine learning Neural networks Original Article Performance evaluation Probability and Statistics in Computer Science Process parameters Kernel-based activation function (KAF) CNN Deep neural network KAF + RSigELUS KAF + RSigELUD
ISSN:	0941-0643, 1433-3058
On-line přístup:	Získat plný text
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