An accelerated MDM algorithm for SVM training
Entidad
UAM. Departamento de Ingeniería InformáticaEditor
Université catholique de LouvainFecha de edición
2008Cita
ESANN 2008: proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges: Université catholique de Louvain, 2008. 421-426ISBN
2-930307-08-0Financiado por
With partial support of Spain’s TIN 2004–07676 and TIN 2007–66862 projects. The first author is kindly supported by FPU-MEC grant reference AP2006-02285.Versión del editor
https://www.elen.ucl.ac.be/esann/proceedings/papers.php?ann=2008Materias
InformáticaNota
This is an electronic version of the paper presented at the 16th European Symposium on Artificial Neural Networks, held in Bruges on 2018Resumen
In this work we will propose an acceleration procedure for the
Mitchell–Demyanov–Malozemov (MDM) algorithm (a fast geometric algorithm
for SVM construction) that may yield quite large training savings.
While decomposition algorithms such as SVMLight or SMO are usually the
SVM methods of choice, we shall show that there is a relationship between
SMO and MDM that suggests that, at least in their simplest implementations,
they should have similar training speeds. Thus, and although we
will not discuss it here, the proposed MDM acceleration might be used as
a starting point to new ways of accelerating SMO.
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Google Scholar:Barbero Jiménez, Álvaro
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López Lázaro, Jorge
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Dorronsoro Ibero, José Ramón
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