A grid-quadtree model selection method for support vector machines

作者：

Highlights：

• The quadtree does not evaluate unnecessary regions of the hyperparameters space.

• The grid-quadtree is efficient and fast to determine parameters for large data sets.

• The quadtree significantly decreases the computational time of the grid search.

• The parameters determined by the grid-quadtree provides high accuracy for the SVM.

摘要

•The quadtree does not evaluate unnecessary regions of the hyperparameters space.•The grid-quadtree is efficient and fast to determine parameters for large data sets.•The quadtree significantly decreases the computational time of the grid search.•The parameters determined by the grid-quadtree provides high accuracy for the SVM.

论文关键词：Support vector machine,Parameter determination,Quadtree,Grid search

论文评审过程：Received 19 December 2018, Revised 23 December 2019, Accepted 29 December 2019, Available online 30 December 2019, Version of Record 7 January 2020.

论文官网地址：https://doi.org/10.1016/j.eswa.2019.113172