Computer Algebra in Scientific Computing - 25th International Workshop, CASC 2023, Havana, Cuba, August 28 - September 1, 2023, Proceedings
Computer Algebra in Scientific Computing - 25th International Workshop, CASC 2023, Havana, Cuba, August 28 - September 1, 2023, Proceedings
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科学计算中的计算机代数 - 第 25 届国际研讨会，CASC 2023，古巴哈瓦那，2023 年 8 月 28 日至 9 月 1 日，会议记录
DOI：
10.1007/978-3-031-41724-5_2
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Barket R
中科院分区：
文献类型：
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作者：
Barket R
近年来，机器学习在计算机代数领域得到了越来越多的应用，包括在符号积分这一重要的子领域。然而，机器学习模型需要大量数据才能取得成功，而且所需规模的基准很少。虽然生成新数据的方法已经存在，但它们在几个方面存在缺陷，可能导致在基于它们的机器学习模型中出现偏差。在这篇文章中，我们描述了如何使用符号积分的Risch算法来创建初等可积表达式的数据集。此外，我们还表明，以这种方式生成的数据缓解了早期方法中发现的一些缺陷。
There has been an increasing number of applications of machine learning to the field of Computer Algebra in recent years, including to the prominent sub-field of Symbolic Integration. However, machine learning models require an abundance of data for them to be successful and there exist few benchmarks on the scale required. While methods to generate new data already exist, they are flawed in several ways which may lead to bias in machine learning models trained upon them. In this paper, we describe how to use the Risch Algorithm for symbolic integration to create a dataset of elementary integrable expressions. Further, we show that data generated this way alleviates some of the flaws found in earlier methods.