Publication Details
SVM CLASSIFIERS CREATION IN PARALLEL CONSTRAINED ENVIRONMENT
Zemčík Pavel, prof. Dr. Ing., dr. h. c. (DCGM)
Herout Adam, prof. Ing., Ph.D. (DCGM)
Beran Vítězslav, doc. Ing., Ph.D. (DCGM)
Support Vector Machine, SVM, Sun Grid Engine, dataset, Feature vectors, Parametric training
Support Vector Machines (SVM) classification is one of the most frequently used classification methods based on machine learning used today. SVMs, however, are dependent on many parameters and settings and so it is suitable to perform the learning process in many instances and evaluate what parameters and settings are suitable for each individual case of data and task. This paper focuses on a novel framework that allows parametric training of SVM classifiers in parallel computer environment which has certain constraints regarding the resources available to the training task and duration of it. The framework is introduced and conclusions are drawn.
This paper focuses on a novel framework that allows parametric training of SVM classifiers in parallel computer environment which has certain constraints regarding the resources available to the training task and duration of it.
@inproceedings{BUT34829,
author="Ivo {Řezníček} and Pavel {Zemčík} and Adam {Herout} and Vítězslav {Beran}",
title="SVM CLASSIFIERS CREATION IN PARALLEL CONSTRAINED ENVIRONMENT",
booktitle="Proc. of the IADIS Int. Conf. - Computer Graphics, Visualization, Computer Vision and Image Processing, CGVCVIP 2010, Visual Commun., VC 2010, Web3DW 2010, Part of the MCCSIS 2010",
year="2010",
pages="535--538",
publisher="IADIS",
address="Freiburg im Breissgau",
isbn="978-972-8939-22-9"
}