Transfer Learning to Adapt One Class SVM Detection to Additional Features

Yongjian Xue, Pierre Beauseroy

2018

Abstract

In this paper, we use the multi-task learning idea to solve a problem of detection with one class SVM when new sensors are added to the system. The main idea is to adapt the detection system to the upgraded sensor system. To solve that problem, the kernel matrix of multi-task learning model can be divided into two parts, one part is based on the former features and the other part is based on the new features. Typical estimation methods can be used to fill the corresponding new features in the old detection system, and a variable kernel is used for the new features in order to balance the importance of the new features with the number of observed samples. Experimental results show that it can keep the false alarm rate relatively stable and decrease the miss alarm rate rapidly as the number of samples increases in the target task.

Paper Citation

in Harvard Style

Xue Y. and Beauseroy P. (2018). Transfer Learning to Adapt One Class SVM Detection to Additional Features.In Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-276-9, pages 78-85. DOI: 10.5220/0006553200780085

in Bibtex Style

@conference{icpram18,
author={Yongjian Xue and Pierre Beauseroy},
title={Transfer Learning to Adapt One Class SVM Detection to Additional Features},
booktitle={Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2018},
pages={78-85},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006553200780085},
isbn={978-989-758-276-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Transfer Learning to Adapt One Class SVM Detection to Additional Features
SN - 978-989-758-276-9
AU - Xue Y.
AU - Beauseroy P.
PY - 2018
SP - 78
EP - 85
DO - 10.5220/0006553200780085