Name
Title | ||
|---|---|---|
Investigating Convergence Of Linear Svm Implemented In Permonsvm Employing Mprgp Algorithm |
Abstract | ||
|---|---|---|
This paper deals with the novel PermonSVM machine learning tool. PermonSVM is a part of our PERMON toolbox. It implements the linear two-class Support Vector Machines. PermonSVM is built on top of PermonQP (PERMON module for quadratic programming) which in turn uses PETSc. The main advantage of PermonSVM is that it is parallel. The parallelism comes from a distribution of matrices and vectors. The MPRGP algorithm, implemented in PermonQP, is used as a solver of the quadratic programming problem arising from the dual SVM formulation. The scalability of MPRGP was proven in problems of mechanics with more than billion of unknowns solved on tens of thousands of cores. Apart from the scalability of our approach, we also investigate the relations between training rate, hyperplane margin, the value of the dual functional, and the norm of the projected gradient. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-97136-0_9 | HIGH PERFORMANCE COMPUTING IN SCIENCE AND ENGINEERING, HPCSE 2017 |
Keywords | Field | DocType |
Support Vector Machines, SVM, PERMON, PermonSVM, PermonQP, MPRGP, Quadratic programming, QP | Convergence (routing),Computer science,Matrix (mathematics),Support vector machine,Toolbox,Parallel computing,Algorithm,Solver,Quadratic programming,Hyperplane,Scalability | Conference |
Volume | ISSN | Citations |
11087 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 3 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jakub Kruzík | 1 | 6 | 0.83 |
| Marek Pecha | 2 | 0 | 0.68 |
| Václav Hapla | 3 | 22 | 5.30 |
| David Horák | 4 | 35 | 6.79 |
| martin cermak | 5 | 11 | 4.44 |