Investigating convergence of linear SVM implemented in PermonSVM employing MPRGP algorithm

Result code in 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>
Alternative codes found
RIV/68145535:_____/18:00495870 RIV/61989100:27240/18:10239488 RIV/61989100:27730/18:10239488 RIV/61989100:27740/18:10239488
Result on the web
<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>

Result language
angličtina
Original language name
Investigating convergence of linear SVM implemented in PermonSVM employing MPRGP algorithm
Original language description
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.
Czech name
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Czech description
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Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
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
Number of pages
15
Pages from-to
115-129
Publisher name
Springer
Place of publication
Cham
Event location
Karolinka
Event date
May 22, 2017
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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