15th workshop gRFIA
9th Music Encoding Conference (MEI 2021)
Alicante, July 19-23
13th international workshop on Machine Learning and Music
(online) September 18, 2020
- Rico-Juan, J. R.; Micó, L.
"Comparison of AESA and LAESA search algorithms using string and tree edit distances"
Pattern Recognition Letters, vol. 24(9), pp. 1427-1436
Although the success rate of handwritten character recognition using a
nearest neighbour technique together with edit distance is satisfactory, the
exhaustive search is expensive. Some fast methods as AESA and LAESA have been
proposed to find nearest neighbours in metric spaces. The average number of
distances computed by these algorithms is very low and does not depend on
the number of prototypes in the training set. In this paper, we compare the
behaviour of these algorithms when string and tree edit distances are used.
author = "Rico-Juan, J. R.; Micó, L.",
title = "Comparison of AESA and LAESA search algorithms using string and tree edit distances",
journal = "Pattern Recognition Letters",
pages = "1427-1436",
volume = "24(9)",
year = "2003"