Title: | Handwritten digit recognition by support vector machine optimized by Bat algorithm |
Authors: | Tuba, Eva Tuba, Milan Simian, Dana |
Citation: | WSCG '2016: short communications proceedings: The 24th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision 2016 in co-operation with EUROGRAPHICS: University of West Bohemia, Plzen, Czech RepublicMay 30 - June 3 2016, p. 369-376. |
Issue Date: | 2016 |
Publisher: | Václav Skala - UNION Agency |
Document type: | konferenční příspěvek conferenceObject |
URI: | wscg.zcu.cz/WSCG2016/!!_CSRN-2602.pdf http://hdl.handle.net/11025/29725 |
ISBN: | 978-80-86943-58-9 |
ISSN: | 2464-4617 |
Keywords: | digitální rozpoznávání ručního písma;inteligence rojů;bat algoritmus;podpůrný vektorový stroj;ladění parametrů |
Keywords in different language: | handwritten digit recognition;swarm intelligence;bat algorithm;support vector machine;parameter tuning |
Abstract: | Handwritten digit recognition is an important but very hard practical problem. This is a classification problem for which support vector machines are very successfully used. Determining optimal support vector machine is another hard optimization problem that involves tuning of the soft margin and kernel function parameters. For this optimization we adjusted recent swarm intelligence bat algorithm. We intentionally used weak set of features, four histogram projections, to prove that even under unfavorable conditions our algorithm would achieve acceptable results. We tested our approach on standard MNIST benchmark datasets and compared the results with other recent approaches from literature where our proposed algorithm achieved better results i.e. higher correct classification percentage. |
Rights: | © Václav Skala - UNION Agency |
Appears in Collections: | WSCG '2016: Short Papers Proceedings |
Please use this identifier to cite or link to this item:
http://hdl.handle.net/11025/29725
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