Title:	SVM CLASSIFIERS CREATION IN PARALLEL CONSTRAINED ENVIRONMENT
Author(s):	Ivo Reznicek, Pavel Zemcik, Adam Herout, Vitezslav Beran
ISBN:	978-972-8939-22-9
Editors:	Yingcai Xiao, Tomaz Amon and Piet Kommers
Year:	2010
Edition:	Single
Keywords:	Support Vector Machine, SVM, Sun Grid Engine, dataset, Feature vectors, Parametric training
Type:	Poster / Demonstration
First Page:	535
Last Page:	358
Language:	English
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Full Contents:	click to dowload
Paper Abstract:	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.
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