Publication detail

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

MUSIL, M.

Original Title

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

English Title

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

Type

conference paper

Language

en

Original Abstract

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.

English abstract

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.

Keywords

computer-vision, Histogram of Oriented Gradients (HOG), optimization, performance, rail candidates detection, run-time complexity, Support Vector Machine (SVM)

RIV year

2015

Released

28.12.2015

Publisher

Akademické sdružení MAGNANIMITAS Assn.

Location

Hradec Králové

ISBN

978-80-87952-12-2

Book

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

Edition

vol. VI

Edition number

NEUVEDEN

Pages from

2138

Pages to

2146

Pages count

9

URL

Documents

BibTex


@inproceedings{BUT123624,
  author="Marek {Musil}",
  title="Reducing the Run-time Complexity of Support Vector Machine Used for Rail Candidates Detection",
  annote="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.",
  address="Akademické sdružení MAGNANIMITAS Assn.",
  booktitle="International Masaryk conference for Ph.D. students and young researchers",
  chapter="123624",
  edition="vol. VI",
  howpublished="online",
  institution="Akademické sdružení MAGNANIMITAS Assn.",
  year="2015",
  month="december",
  pages="2138--2146",
  publisher="Akademické sdružení MAGNANIMITAS Assn.",
  type="conference paper"
}