Erzeugungsgrad, VC-dimension and neural networks with rational activation function

Luis Miguel Pardo; Daniel Sebastián

doi:10.1007/s00200-025-00723-4

January 28, 2026Applicable Algebra in Engineering Communication and Computing

Erzeugungsgrad, VC-dimension and neural networks with rational activation function

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Key Points

Improved generalization bounds arise from analyzing the VC-dimension of neural networks, enhancing performance.
The investigation reveals that rational activation functions lead to significant changes in the efficiency of learning.
Analysis focuses on the interplay between VC-dimension and the representation ability of neural networks.
Ultimately, this work highlights the potential of using rational activation functions for optimized model training.

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Implication

The analysis explores how VC-dimension influences performance in neural networks with rational activation functions, suggesting improved generalization.

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Pardo et al. (2026) studied this question.

synapsesocial.com/papers/69a75bdec6e9836116a23f87 https://doi.org/10.1007/s00200-025-00723-4

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Erzeugungsgrad, VC-dimension and neural networks with rational activation function

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Erzeugungsgrad, VC-dimension and neural networks with rational activation function

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