研究人员开发了两种新的符号回归方法，这是一种用于查找拟合数据的数学表达式的技术。一种方法，潜在方程嵌入（LEE），在潜在空间中使用迭代细化来提高准确性并降低表达式复杂度，在基准测试中优于现有方法。另一种方法，多样化残差符号回归（DRSR），侧重于生成多个不同的表达式来解释不同的残差模式，有助于选择与领域知识一致并有效处理异常值的模型。 AI
影响 符号回归的这些进步可能导致更具可解释性和更准确的模型，用于科学发现和数据分析。
排序理由 该集群包含两篇详细介绍符号回归新方法的学术论文。
在 arXiv cs.NE (Neural & Evolutionary) 阅读 →
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arXiv:2605.27245v1 Announce Type: new Abstract: Symbolic regression (SR) seeks closed-form mathematical expressions that fit observed data. Neural SR methods amortize the search by training an encoder to map observations directly to expressions in a single pass, but this amortize…
Symbolic regression (SR) seeks closed-form mathematical expressions that fit observed data. Neural SR methods amortize the search by training an encoder to map observations directly to expressions in a single pass, but this amortized inference leaves a residual amortization gap b…
Symbolic regression (SR) seeks closed-form mathematical expressions that fit observed data. Neural SR methods amortize the search by training an encoder to map observations directly to expressions in a single pass, but this amortized inference leaves a residual amortization gap b…
Symbolic regression (SR) aims to discover explicit mathematical expressions that explain observed data and is widely used in domains where interpretability is essential. Because interpretability requires expressions to reflect meaningful regularities, SR is sensitive to observati…