Detail publikace

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

Originální název

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

Anglický název

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

Jazyk

en

Originální abstrakt

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.

Anglický abstrakt

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.

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"
}