Reducing the Run-time Complexity of Support Vector Machine Used for Rail Candidates Detection

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F15%3APU118626" target="_blank" >RIV/00216305:26230/15:PU118626 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.vedeckekonference.cz/index.php?option=com_content&view=article&id=79&Itemid=66&lang=en" target="_blank" >http://www.vedeckekonference.cz/index.php?option=com_content&view=article&id=79&Itemid=66&lang=en</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Reducing the Run-time Complexity of Support Vector Machine Used for Rail Candidates Detection

Popis výsledku v původním jazyce

Support Vector Machine (SVM) is a technique for classification and regression. It uses a decision surface called hyperplane that depends on the regularization parameter and training points lying in the margin of the hyperplane. The run-time complexity of SVM may be reduced through the hyperplane affected by the regularization parameter. We deal with rails recognition in images taken from the camera mounted on the board of the locomotive. For the purpose of rail candidates detection, we deployed an algorithm using SVM. We performed several experiments under different settings. In this paper, we introduce an algorithm using SVM and the impact of its regulation parameter as well as others possible on SVM-performance. The main goal is to decrease time-complexity while maintaining classification success rate.

Název v anglickém jazyce

Reducing the Run-time Complexity of Support Vector Machine Used for Rail Candidates Detection

Popis výsledku anglicky

Support Vector Machine (SVM) is a technique for classification and regression. It uses a decision surface called hyperplane that depends on the regularization parameter and training points lying in the margin of the hyperplane. The run-time complexity of SVM may be reduced through the hyperplane affected by the regularization parameter. We deal with rails recognition in images taken from the camera mounted on the board of the locomotive. For the purpose of rail candidates detection, we deployed an algorithm using SVM. We performed several experiments under different settings. In this paper, we introduce an algorithm using SVM and the impact of its regulation parameter as well as others possible on SVM-performance. The main goal is to decrease time-complexity while maintaining classification success rate.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/ED1.1.00%2F02.0070" target="_blank" >ED1.1.00/02.0070: Centrum excelence IT4Innovations</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2015

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

International Masaryk conference for Ph.D. students and young researchers

ISBN

978-80-87952-12-2

ISSN

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e-ISSN

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Počet stran výsledku

9

Strana od-do

2138-2146

Název nakladatele

Akademické sdružení MAGNANIMITAS Assn.

Místo vydání

Hradec Králové

Místo konání akce

Hradec Králové

Datum konání akce

14. 12. 2015

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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