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arXiv 2103.11249cs.SEcs.AI
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SELM:机器学习模型的软件工程

SELM: Software Engineering of Machine Learning Models

Malek Ashtar University of Technology(马利克阿什塔尔理工大学);Sharif University of Technology(谢里夫理工大学)

Nafiseh Jafari, Mohammad Reza Besharati, Mohammad Izadi, Maryam Hourali

摘要:

任何机器学习模型的支柱之一是它的概念。使用软件工程,我们可以设计这些概念,然后开发和扩展它们。在这篇文章中,我们提出了一个SELM框架的机器学习模型的软件工程。然后,我们通过一个案例研究来评估这个框架。使用SELM框架,我们可以提高机器学习过程的效率,并以更少的处理硬件资源和更小的训练数据集提供更高的学习精度。这个问题突出了跨学科方法对机器学习的重要性。因此,在本文中,我们提供了跨学科团队对机器学习的建议。

英文摘要:

One of the pillars of any machine learning model is its concepts. Using software engineering, we can engineer these concepts and then develop and expand them. In this article, we present a SELM framework for Software Engineering of machine Learning Models. We then evaluate this framework through a case study. Using the SELM framework, we can improve a machine learning process efficiency and provide more accuracy in learning with less processing hardware resources and a smaller training dataset. This issue highlights the importance of an interdisciplinary approach to machine learning. Therefore, in this article, we have provided interdisciplinary teams' proposals for machine learning.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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