论文标题
具有符号回归的可解释科学发现：评论
Interpretable Scientific Discovery with Symbolic Regression: A Review
论文作者
论文摘要
符号回归正在作为一种有前途的机器学习方法，用于直接从数据中学习可解释的数学表达的简洁基础。尽管传统上已经通过基因编程进行了解决，但它最近作为数据驱动的模型发现方法引起了人们对深度学习的日益兴趣，在从基本到应用科学的各种应用领域中取得了重大进步。这项调查介绍了符号回归方法的结构化和全面的概述，并讨论了它们的优势和局限性。
Symbolic regression is emerging as a promising machine learning method for learning succinct underlying interpretable mathematical expressions directly from data. Whereas it has been traditionally tackled with genetic programming, it has recently gained a growing interest in deep learning as a data-driven model discovery method, achieving significant advances in various application domains ranging from fundamental to applied sciences. This survey presents a structured and comprehensive overview of symbolic regression methods and discusses their strengths and limitations.
