2017/06/17 by Zhong Wang, Tinyi Chu, Wang, Zhong +5
Computer Science · Environmental Science · #Data Analysis with R #FOS: Computer and information sciences #Hydrological Forecasting Using AI #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural Networks and Applications
paper · pdf · doi:10.48550/arxiv.1706.05544
openalex publication_date 2017/06/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Rgtsvm provides a fast and flexible support vector machine (SVM) implementation for the R language. The distinguishing feature of Rgtsvm is that support vector classification and support vector regression tasks are implemented on a graphical processing unit (GPU), allowing the libraries to scale to millions of examples with >100-fold improvement in performance over existing implementations. Nevertheless, Rgtsvm retains feature parity and has an interface that is compatible with the popular e1071 SVM package in R. Altogether, Rgtsvm enables large SVM models to be created by both experienced and novice practitioners.