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Authors: Andrea Baisero ; Florian T. Pokorny ; Danica Kragic and Carl Henrik Ek

Affiliation: KTH Royal Institute of Technology, Sweden

ISBN: 978-989-8565-41-9

Keyword(s): Kernel Methods, Sequential Modelling.

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

Abstract: Kernel methods have been used very successfully to classify data in various application domains. Traditionally, kernels have been constructed mainly for vectorial data defined on a specific vector space. Much less work has been addressing the development of kernel functions for non-vectorial data. In this paper, we present a new kernel for encoding sequential data. We present our results comparing the proposed kernel to the state of the art, showing a significant improvement in classification and a much improved robustness and interpretability.

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Paper citation in several formats:
Baisero, A.; T. Pokorny, F.; Kragic, D. and Henrik Ek, C. (2013). The Path Kernel.In Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-8565-41-9, pages 50-57. DOI: 10.5220/0004267300500057

@conference{icpram13,
author={Andrea Baisero. and Florian T. Pokorny. and Danica Kragic. and Carl Henrik Ek.},
title={The Path Kernel},
booktitle={Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2013},
pages={50-57},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004267300500057},
isbn={978-989-8565-41-9},
}

TY - CONF

JO - Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - The Path Kernel
SN - 978-989-8565-41-9
AU - Baisero, A.
AU - T. Pokorny, F.
AU - Kragic, D.
AU - Henrik Ek, C.
PY - 2013
SP - 50
EP - 57
DO - 10.5220/0004267300500057

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