Proceedings Paper
Optimization of support vector machine hyperparameters using radius/margin bound| Format | Member Price | Non-Member Price |
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Paper Abstract
The paper presents the method of tuning the support vector machine hyperparameters by minimizing an
estimate of the leave-one-out error known as radius/margin bound. The quality of the method, in terms of classification
accuracy and generalization rate was tested against an exhaustive grid-search in hyperparameter space using a 2-
dimensional Banana dataset.
Paper Details
Date Published: 15 October 2012
PDF: 8 pages
Proc. SPIE 8454, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2012, 845423 (15 October 2012); doi: 10.1117/12.2002318
Published in SPIE Proceedings Vol. 8454:
Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2012
Ryszard S. Romaniuk, Editor(s)
PDF: 8 pages
Proc. SPIE 8454, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2012, 845423 (15 October 2012); doi: 10.1117/12.2002318
Show Author Affiliations
Stanisław Jankowski, Warsaw Univ. of Technology (Poland)
Wojciech Sadurski, Warsaw Univ. of Technology (Poland)
Published in SPIE Proceedings Vol. 8454:
Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2012
Ryszard S. Romaniuk, Editor(s)
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