Fast Function Extraction (FFX) is a deterministic algorithm for solving symbolic regression problems. We improve the accuracy of FFX by adding parameters to arguments nonlinear functions. Instead only optimizing linear parameters, we optimize these additional with separable least squared optimization using variable projection algorithm. Both and our new applied on PennML benchmark suite. show t...

Online learning has witnessed an increasing interest over the recent past due to its low computational requirements and relevance a broad range of streaming applications. In this brief, we focus on online regularized regression. We propose novel efficient regression algorithm, called normalized least-squares (ONLS). perform theoretical analysis by comparing total loss ONLS against gradient desc...

The least median of squares estimator (Rousseeuw, 1984)1 of linear regression parameters is a high breakdown estimator, meaning that, unlike the least squares estimator, it performs reasonably well when up to 50% outliers are present in a data set. Unfortunately, it lacks efficiency under normal errors. This disadvantage can be overcome by using the least median of squares estimator as a starti...