论文标题
具有快速功能提取和非线性最小二乘优化的符号回归
Symbolic Regression with Fast Function Extraction and Nonlinear Least Squares Optimization
论文作者
论文摘要
快速功能提取（FFX）是用于解决符号回归问题的确定性算法。我们通过将参数添加到非线性函数的参数中提高了FFX的准确性。我们不仅可以优化线性参数，还使用可分离的非线性最小二乘优化优化了这些附加的非线性参数。 FFX和我们的新算法都应用于PenNML基准套件。我们表明，提出的FFX扩展可以提高准确性，同时提供相似长度的模型，并且在给定数据上的运行时仅增加了。将我们的结果与已经为给定基准套件发布的大量回归方法进行了比较。
Fast Function Extraction (FFX) is a deterministic algorithm for solving symbolic regression problems. We improve the accuracy of FFX by adding parameters to the arguments of nonlinear functions. Instead of only optimizing linear parameters, we optimize these additional nonlinear parameters with separable nonlinear least squared optimization using a variable projection algorithm. Both FFX and our new algorithm is applied on the PennML benchmark suite. We show that the proposed extensions of FFX leads to higher accuracy while providing models of similar length and with only a small increase in runtime on the given data. Our results are compared to a large set of regression methods that were already published for the given benchmark suite.
