Detail publikace

SVM CLASSIFIERS CREATION IN PARALLEL CONSTRAINED ENVIRONMENT

Originální název

SVM CLASSIFIERS CREATION IN PARALLEL CONSTRAINED ENVIRONMENT

Anglický název

SVM CLASSIFIERS CREATION IN PARALLEL CONSTRAINED ENVIRONMENT

Jazyk

en

Originální abstrakt

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.

Anglický abstrakt

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.

BibTex


@inproceedings{BUT34829,
  author="Ivo {Řezníček} and Pavel {Zemčík} and Adam {Herout} and Vítězslav {Beran}",
  title="SVM CLASSIFIERS CREATION IN PARALLEL CONSTRAINED ENVIRONMENT",
  annote="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.",
  address="IADIS",
  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",
  chapter="34829",
  edition="NEUVEDEN",
  howpublished="print",
  institution="IADIS",
  year="2010",
  month="december",
  pages="535--538",
  publisher="IADIS",
  type="conference paper"
}