ON STOCHASTIC TREE DISTANCES AND THEIR TRAINING VIA EXPECTATION-MAXIMISATION

Martin Emms

Abstract

Continuing a line of work initiated in (Boyer et al., 2007), the generalisation of stochastic string distance to a stochastic tree distance is considered. We point out some hitherto overlooked necessary modifications to the Zhang/Shasha tree-distance algorithm for all-paths and viterbi variants of this stochastic tree distance. A strategy towards an EM cost-adaptation algorithm for the all-paths distance which was suggested by (Boyer et al., 2007) is shown to overlook necessary ancestry preservation constraints, and an alternative EM costadaptation algorithm for the Viterbi variant is proposed. Experiments are reported on in which a distanceweighted kNN categorisation algorithm is applied to a corpus of categorised tree structures. We show that a 67.7% base-line using standard unit-costs can be improved to 72.5% by the EM cost adaptation algorithm.

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Paper Citation


in Harvard Style

Emms M. (2012). ON STOCHASTIC TREE DISTANCES AND THEIR TRAINING VIA EXPECTATION-MAXIMISATION . In Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-8425-98-0, pages 144-153. DOI: 10.5220/0003864901440153


in Bibtex Style

@conference{icpram12,
author={Martin Emms},
title={ON STOCHASTIC TREE DISTANCES AND THEIR TRAINING VIA EXPECTATION-MAXIMISATION},
booktitle={Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2012},
pages={144-153},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003864901440153},
isbn={978-989-8425-98-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - ON STOCHASTIC TREE DISTANCES AND THEIR TRAINING VIA EXPECTATION-MAXIMISATION
SN - 978-989-8425-98-0
AU - Emms M.
PY - 2012
SP - 144
EP - 153
DO - 10.5220/0003864901440153