Rgtsvm: Support Vector Machines on a GPU in R

06/17/2017
by   Zhong Wang, et al.
0

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.

READ FULL TEXT
research
05/01/2019

High-Performance Support Vector Machines and Its Applications

The support vector machines (SVM) algorithm is a popular classification ...
research
03/04/2014

EnsembleSVM: A Library for Ensemble Learning Using Support Vector Machines

EnsembleSVM is a free software package containing efficient routines to ...
research
05/01/2021

Comprehensive Review On Twin Support Vector Machines

Twin support vector machine (TSVM) and twin support vector regression (T...
research
08/17/2018

A bagging and importance sampling approach to Support Vector Machines

An importance sampling and bagging approach to solving the support vecto...
research
03/06/2020

Application of Support Vector Machines for Seismogram Analysis and Differentiation

Support Vector Machines (SVM) is a computational technique which has bee...
research
02/22/2017

liquidSVM: A Fast and Versatile SVM package

liquidSVM is a package written in C++ that provides SVM-type solvers for...
research
08/08/2020

GPU-Accelerated Primal Learning for Extremely Fast Large-Scale Classification

One of the most efficient methods to solve L2-regularized primal problem...

Please sign up or login with your details

Forgot password? Click here to reset