论文标题
M-Arcsinh：Scikit-Learn中SVM和MLP的有效且可靠的功能
m-arcsinh: An Efficient and Reliable Function for SVM and MLP in scikit-learn
论文作者
论文摘要
本文描述了逆双曲正弦函数（'arcsinh'）的修改（'M-'）版本的“ M-Arcsinh”。内核和激活功能使机器学习（ML）的算法（例如支持向量机（SVM）和多层感知器（MLP））以监督方式从数据中学习。在开源Python库“ Scikit-learn”中实现的M-Arcsinh在此分别作为SVM和MLP的有效且可靠的内核和激活函数。讨论了Scikit-Learn和California Irvine（UCI）机器学习存储库中15（n = 15）数据集的分类任务的可靠性和速度的提高。实验结果表明，通过建议的函数实现了SVM和MLP的总体竞争分类性能。将此功能与黄金标准内核和激活功能进行比较，并证明其整体竞争可靠性不管涉及的分类任务的复杂性如何。
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.
