Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: m-arcsinh: An Efficient and Reliable Function for SVM and MLP in
  scikit-learn   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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The categories of this paper are: supr-con
14
The categories of this paper are: math-ph
4
The categories of this paper are: cond-mat
8
The categories of this paper are: hep-ex
7
The categories of this paper are: hep-th
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The categories of this paper are: funct-an
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The categories of this paper are: chem-ph
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The categories of this paper are: q-fin
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The categories of this paper are: cmp-lg
13
The categories of this paper are: quant-ph
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The categories of this paper are: q-alg
23
The categories of this paper are: solv-int
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The categories of this paper are: nucl-th
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The categories of this paper are: bayes-an
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The categories of this paper are: alg-geom
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The categories of this paper are: dg-ga