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2024 | OriginalPaper | Chapter

Hilbert Basis Activation Function for Neural Network

Authors : J. E. Souza de Cursi, A. El Mouatasim, T. Berroug, R. Ellaia

Published in: Proceedings of the 6th International Symposium on Uncertainty Quantification and Stochastic Modelling

Publisher: Springer International Publishing

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Abstract

The chapter delves into the introduction of a novel Hilbert basis activation function for neural networks, drawing on concepts from algebraic geometry. It discusses the formulation and properties of the Hilbert basis activation function, highlighting its ability to capture local geometric structures and adapt to data characteristics. The authors present experimental results comparing the new activation function with popular alternatives like ReLU and sigmoid, demonstrating its competitive performance. The chapter also explores the potential applications and future research directions for this innovative activation function, making it a compelling read for specialists in the field.
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Metadata
Title
Hilbert Basis Activation Function for Neural Network
Authors
J. E. Souza de Cursi
A. El Mouatasim
T. Berroug
R. Ellaia
Copyright Year
2024
Book
Proceedings of the 6th International Symposium on Uncertainty Quantification and Stochastic Modelling

Print ISBN: 978-3-031-47035-6

Electronic ISBN: 978-3-031-47036-3

Copyright Year: 2024

DOI
https://doi.org/10.1007/978-3-031-47036-3_22

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