m-arcsinh：scikit-learn中SVM和MLP的高效可靠函数
m-arcsinh: An Efficient and Reliable Function for SVM and MLP in scikit-learn
摘要：
本文描述了“m-arcsinh”，反双曲正弦函数（“arcsinh”）的修改（“m-”）版本。核函数和激活函数使基于机器学习（ML）的算法（如支持向量机（SVM）和多层感知器（MLP））能够以监督的方式从数据中学习。在开源Python库“scikit-learn”中实现的m-arcsinh在此分别作为SVM和MLP的高效可靠的内核和激活函数提出。讨论了scikit-learn和加州大学欧文分校（UCI）机器学习存储库中15个（N = 15）数据集上分类任务的可靠性和收敛速度的改进。实验结果表明，整体竞争力的分类性能的支持向量机和MLP，通过所提出的功能实现。此功能相比，黄金标准内核和激活函数，展示了其整体的竞争力的可靠性，无论所涉及的分类任务的复杂性。
英文摘要：
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.
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