2021/11/21 by Leevan Ling, Ling, Leevan, Francesco Marchetti +1 · 1 citation
Computer Science · Engineering · #FOS: Mathematics #Machine Learning and Algorithms #Medical Image Segmentation Techniques #Non-Destructive Testing Techniques #Numerical Analysis (math.NA)
paper · pdf · doi:10.48550/arxiv.2111.10782
openalex publication_date 2021/11/21 · openalex created_date 2021/12/06 · openalex updated_date 2026/07/28
In kernel-based approximation, the tuning of the so-called shape parameter is a fundamental step for achieving an accurate reconstruction. Recently, the popular Rippa's algorithm [14] has been extended to a more general cross validation setting. In this work, we propose a modification of such extension with the aim of further reducing the computational costs. The resulting Stochastic Extended Rippa's Algorithm (SERA) is first detailed and then tested by means of various numerical experiments, which show its efficacy and effectiveness in different approximation settings.