Discriminative Prior Bias Learning for Pattern Classification

Takumi Kobayashi, Kenji Nishida

2014

Abstract

Prior information has been effectively exploited mainly using probabilistic models. In this paper, by focusing on the bias embedded in the classifier, we propose a novel method to discriminatively learn the prior bias based on the extra prior information assigned to the samples other than the class category, e.g., the 2-D position where the local image feature is extracted. The proposed method is formulated in the framework of maximum margin to adaptively optimize the biases, improving the classification performance. We also present the computationally efficient optimization approach that makes the method even faster than the standard SVM of the same size. The experimental results on patch labeling in the on-board camera images demonstrate the favorable performance of the proposed method in terms of both classification accuracy and computation time.

Paper Citation

in Harvard Style

Kobayashi T. and Nishida K. (2014). Discriminative Prior Bias Learning for Pattern Classification . In Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-018-5, pages 67-75. DOI: 10.5220/0004813600670075

in Bibtex Style

@conference{icpram14,
author={Takumi Kobayashi and Kenji Nishida},
title={Discriminative Prior Bias Learning for Pattern Classification},
booktitle={Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2014},
pages={67-75},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004813600670075},
isbn={978-989-758-018-5},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Discriminative Prior Bias Learning for Pattern Classification
SN - 978-989-758-018-5
AU - Kobayashi T.
AU - Nishida K.
PY - 2014
SP - 67
EP - 75
DO - 10.5220/0004813600670075