Rgtsvm: Support Vector Machines on a GPU in R
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.
Code (1)
Tasks
General ClassificationGPUregressionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Tverberg's theorem and multi-class support vector machines
We show how, using linear-algebraic tools developed to prove Tverberg's theorem in combinatorial geometry, we can design new models of multi-class support vector machines (SVMs). These supervised learning protocols requi…
On Neural Quantum Support Vector Machines
In \cite{simon2023algorithms} we introduced four algorithms for the training of neural support vector machines (NSVMs) and demonstrated their feasibility. In this note we introduce neural quantum support vector machines,…
Insensitive Stochastic Gradient Twin Support Vector Machine for Large Scale Problems
Stochastic gradient descent algorithm has been successfully applied on support vector machines (called PEGASOS) for many classification problems. In this paper, stochastic gradient descent algorithm is investigated to tw…
General ClassificationImproving the Interpretability of Support Vector Machines-based Fuzzy Rules
Support vector machines (SVMs) and fuzzy rule systems are functionally equivalent under some conditions. Therefore, the learning algorithms developed in the field of support vector machines can be used to adapt the param…
Linear Classification of data with Support Vector Machines and Generalized Support Vector Machines
In this paper, we study the support vector machine and introduced the notion of generalized support vector machine for classification of data. We show that the problem of generalized support vector machine is equivalent …
General Classification