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Authors: Xiaoyi Chen and Régis Lengellé

Affiliation: University of Technology of Troyes, France

ISBN: 978-989-758-222-6

Keyword(s): Transfer Learning, Kernel, SVM, Maximum Mean Discrepancy.

Related Ontology Subjects/Areas/Topics: Classification ; Kernel Methods ; Large Margin Methods ; Pattern Recognition ; Theory and Methods

Abstract: This paper is a contribution to solving the domain adaptation problem where no labeled target data is available.A new SVM approach is proposed by imposing a zero-valued Maximum Mean Discrepancy-like constraint.This heuristic allows us to expect a good similarity between source and target data, after projection onto an efficient subspace of a Reproducing Kernel Hilbert Space. Accordingly, the classifier will perform well on source and target data. We show that this constraint does not modify the quadratic nature of the optimization problem encountered in classic SVM, so standard quadratic optimization tools can be used. Experimental results demonstrate the competitiveness and efficiency of our method.

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Paper citation in several formats:
Chen X. and Lengellé R. (2017). Domain Adaptation Transfer Learning by SVM Subject to a Maximum-Mean-Discrepancy-like Constraint.In Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-222-6, pages 89-95. DOI: 10.5220/0006119900890095

@conference{icpram17,
author={Xiaoyi Chen and Régis Lengellé},
title={Domain Adaptation Transfer Learning by SVM Subject to a Maximum-Mean-Discrepancy-like Constraint},
booktitle={Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2017},
pages={89-95},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006119900890095},
isbn={978-989-758-222-6},
}

TY - CONF

JO - Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Domain Adaptation Transfer Learning by SVM Subject to a Maximum-Mean-Discrepancy-like Constraint
SN - 978-989-758-222-6
AU - Chen X.
AU - Lengellé R.
PY - 2017
SP - 89
EP - 95
DO - 10.5220/0006119900890095

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