Investigating convergence of linear SVM implemented in PermonSVM employing MPRGP algorithm

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27120%2F18%3A10239488" target="_blank" >RIV/61989100:27120/18:10239488 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68145535:_____/18:00495870 RIV/61989100:27240/18:10239488 RIV/61989100:27730/18:10239488 RIV/61989100:27740/18:10239488

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-3-319-97136-0_9" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-319-97136-0_9</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-97136-0_9" target="_blank" >10.1007/978-3-319-97136-0_9</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Investigating convergence of linear SVM implemented in PermonSVM employing MPRGP algorithm

Popis výsledku v původním jazyce

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. (C) Springer International Publishing AG, part of Springer Nature 2018.

Název v anglickém jazyce

Investigating convergence of linear SVM implemented in PermonSVM employing MPRGP algorithm

Popis výsledku anglicky

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. (C) Springer International Publishing AG, part of Springer Nature 2018.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10102 - Applied mathematics

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2018

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Volume 11087

ISBN

978-3-319-97135-3

ISSN

0302-9743

e-ISSN

1611-3349

Počet stran výsledku

15

Strana od-do

115-129

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Karolinka

Datum konání akce

22. 5. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—