The Path Kernel

Andrea Baisero, Florian T. Pokorny, Danica Kragic, Carl Henrik Ek

2013

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.

Paper Citation

in Harvard Style

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

in Bibtex Style

@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},
}

in EndNote Style

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