17297

Rgtsvm: Support Vector Machines on a GPU in R

Zhong Wang, Tinyi Chu, Lauren A Choate, Charles G Danko
Baker Institute for Animal Health, Cornell University, Ithaca, NY, USA
arXiv:1706.05544 [stat.ML], (17 Jun 2017)

@article{wang2017rgtsvm,

   title={Rgtsvm: Support Vector Machines on a GPU in R},

   author={Wang, Zhong and Chu, Tinyi and Choate, Lauren A and Danko, Charles G},

   year={2017},

   month={jun},

   archivePrefix={"arXiv"},

   primaryClass={stat.ML}

}

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.
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