2011
Calvo-Zaragoza, J.; Rizo, D.; Iñesta, J. M.
A distance for partially labeled trees Journal Article
In: Lecture Notes in Computer Science, vol. 6669, pp. 492–499, 2011, ISSN: 0302-9743.
Abstract | Links | BibTeX | Tags: DRIMS, MIPRCV
@article{k265,
title = {A distance for partially labeled trees},
author = {J. Calvo-Zaragoza and D. Rizo and J. M. Iñesta},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/265/ibpria11-calvo.pdf},
issn = {0302-9743},
year = {2011},
date = {2011-01-01},
journal = {Lecture Notes in Computer Science},
volume = {6669},
pages = {492--499},
abstract = {Trees are a powerful data structure for representing data for which hierarchical
relations can be defined. It has been applied in a number of fields like
image analysis, natural language processing, protein structure, or music
retrieval, to name a few. Procedures for comparing trees are very relevant
in many tasks where tree representations are involved. The computation of
these measures is usually time consuming and different authors have
proposed algorithms that are able to compute them in a reasonable time,
by means of approximated versions of the similarity measure. Other methods
require that the trees are fully labeled for the distance to be computed.
The measure utilized in this paper is able to deal with trees labeled
only at the leaves that runs in $O(|T_1|times|T_2|)$ time. Experiments and
comparative results are provided.},
keywords = {DRIMS, MIPRCV},
pubstate = {published},
tppubtype = {article}
}
Trees are a powerful data structure for representing data for which hierarchical
relations can be defined. It has been applied in a number of fields like
image analysis, natural language processing, protein structure, or music
retrieval, to name a few. Procedures for comparing trees are very relevant
in many tasks where tree representations are involved. The computation of
these measures is usually time consuming and different authors have
proposed algorithms that are able to compute them in a reasonable time,
by means of approximated versions of the similarity measure. Other methods
require that the trees are fully labeled for the distance to be computed.
The measure utilized in this paper is able to deal with trees labeled
only at the leaves that runs in $O(|T_1|times|T_2|)$ time. Experiments and
comparative results are provided.
2011
Calvo-Zaragoza, J.; Rizo, D.; Iñesta, J. M.
A distance for partially labeled trees Journal Article
In: Lecture Notes in Computer Science, vol. 6669, pp. 492–499, 2011, ISSN: 0302-9743.
Abstract | Links | BibTeX | Tags: DRIMS, MIPRCV
@article{k265,
title = {A distance for partially labeled trees},
author = {J. Calvo-Zaragoza and D. Rizo and J. M. Iñesta},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/265/ibpria11-calvo.pdf},
issn = {0302-9743},
year = {2011},
date = {2011-01-01},
journal = {Lecture Notes in Computer Science},
volume = {6669},
pages = {492--499},
abstract = {Trees are a powerful data structure for representing data for which hierarchical
relations can be defined. It has been applied in a number of fields like
image analysis, natural language processing, protein structure, or music
retrieval, to name a few. Procedures for comparing trees are very relevant
in many tasks where tree representations are involved. The computation of
these measures is usually time consuming and different authors have
proposed algorithms that are able to compute them in a reasonable time,
by means of approximated versions of the similarity measure. Other methods
require that the trees are fully labeled for the distance to be computed.
The measure utilized in this paper is able to deal with trees labeled
only at the leaves that runs in $O(|T_1|times|T_2|)$ time. Experiments and
comparative results are provided.},
keywords = {DRIMS, MIPRCV},
pubstate = {published},
tppubtype = {article}
}
Trees are a powerful data structure for representing data for which hierarchical
relations can be defined. It has been applied in a number of fields like
image analysis, natural language processing, protein structure, or music
retrieval, to name a few. Procedures for comparing trees are very relevant
in many tasks where tree representations are involved. The computation of
these measures is usually time consuming and different authors have
proposed algorithms that are able to compute them in a reasonable time,
by means of approximated versions of the similarity measure. Other methods
require that the trees are fully labeled for the distance to be computed.
The measure utilized in this paper is able to deal with trees labeled
only at the leaves that runs in $O(|T_1|times|T_2|)$ time. Experiments and
comparative results are provided.
relations can be defined. It has been applied in a number of fields like
image analysis, natural language processing, protein structure, or music
retrieval, to name a few. Procedures for comparing trees are very relevant
in many tasks where tree representations are involved. The computation of
these measures is usually time consuming and different authors have
proposed algorithms that are able to compute them in a reasonable time,
by means of approximated versions of the similarity measure. Other methods
require that the trees are fully labeled for the distance to be computed.
The measure utilized in this paper is able to deal with trees labeled
only at the leaves that runs in $O(|T_1|times|T_2|)$ time. Experiments and
comparative results are provided.