This paper describes the 'm-arcsinh', a modified ('m-') version of the inverse hyperbolic sine function ('arcsinh'). Kernel and activation functions enable Machine Learning (ML)-based algorithms, such as Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP), to learn from data in a supervised manner. m-arcsinh, implemented in the open source Python library 'scikit-learn', is hereby presented as an efficient and reliable kernel and activation function for SVM and MLP respectively. Improvements in reliability and speed to convergence in classification tasks on fifteen (N = 15) datasets available from scikit-learn and the University California Irvine (UCI) Machine Learning repository are discussed. Experimental results demonstrate the overall competitive classification performance of both SVM and MLP, achieved via the proposed function. This function is compared to gold standard kernel and activation functions, demonstrating its overall competitive reliability regardless of the complexity of the classification tasks involved.

中文翻译：

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m-arcsinh：scikit-learn 中 SVM 和 MLP 的高效可靠函数

本文介绍了“m-arcsinh”，它是反双曲正弦函数 (“arcsinh”) 的修改 (“m-”) 版本。内核和激活函数使基于机器学习 (ML) 的算法，例如支持向量机 (SVM) 和多层感知器 (MLP)，能够以受监督的方式从数据中学习。m-arcsinh 在开源 Python 库 'scikit-learn' 中实现，特此作为高效可靠的内核和激活函数分别用于 SVM 和 MLP。讨论了 scikit-learn 和加州大学欧文分校 (UCI) 机器学习存储库提供的 15 个 (N = 15) 数据集的分类任务在可靠性和收敛速度方面的改进。实验结果证明了 SVM 和 MLP 的整体竞争分类性能，通过建议的功能实现。将此函数与黄金标准内核和激活函数进行比较，无论所涉及的分类任务的复杂性如何，都证明了其整体竞争可靠性。