Acc. Int. E-A ARFAI DOREMI DRIMS ENJAMBRE GRE-12-34 GRE16-14 GRE19-04 GV-JRRJ GV/2020/030 HispaMus ISIC 2010 Leonardo2021 MIPRCV MultiScore PASCAL2 Prometeo 2012 PROSEMUS REPERTORIUM ROMA TADMar TIASA TIMuL UA-CPS

All years 20242023202220212020201920182017201620152014201320122011201020092008200720062005200420032002200120001999199819971996199519941993199219911990198919880000 All types Journal ArticlesBooksConferencesBook ChaptersProceedings ArticlesPhD ThesesPresentationsTechnical Reports All authors Abreu, J. Adriaans, P. Alacid, B. Alashhab, S. Albarrán, P. Alfaro-Contreras, M. Alonso-Jiménez, P. Anagnostopoulou, C. Arista, N. R. Arroyo, V. Asins, E. Ayllon, E. Aznar, I. Balboa, R. M. Barbancho, A. M. Barbancho, I. Barber, F. Barber, X. Baró, A. Barrachina, R. O. Bellet, A. Belmonte, M. A. Benedí, J. Benetos, E. Berlanga, R. Bernabeu, J. F. Bernabeu, M. Bosch, V. Boyer, L. Bresson, J. Buendía, M. Bustos, A. Calera-Rubio, J. Calvo-Zaragoza, J. Cano-Espinosa, C. Canteras, M. Caparrós, M. Cardoso, J. S. Carrasco, R. C. Casacuberta, F. Castellanos, A. Castellanos, F. J. Castellanos, Francisco José Cazorla, M. Cervera, J. M. Cibirían, R. Coltell, O. Compañ-Rosique, P. Conklin, D. Corbí-Bellot, A. M. Costa, A. Cuenca-Asensi, S. Cuevas-Velasquez, H. Dalmau-Espert, J. L. Danger, R. Delgado, T. Desmond, K. Domenech, J. Doménech, S. Domingo, A. Efford, N. Escalona, F. Espí, D. Esplà-Gomis, M. Esposito, Y. Ezquerro, A. Falcone, G. Ferri, F. Fiore, T. M. M. Fisher, R. B. Forcada, M. L. Fornés, A. Fuente, Cde la Fuente, C. Fuente, C. Fujinaga, I. Galant, J. Gallego, A. J. Gallego-Sánchez, A. J. Gallego-Sánchez, J. Garay-Maestre, U. García, P. García-Doménech, R. V. García-García, F. García-Iasci, P. Garrido, A. Garrido-Alenda, A. Garrido-Munoz, C. Gil, P. Gil, R. Gilabert-Zarco, P. Gómez, E. Gómez, J. A. Gómez-Ballester, E. Gomez-Donoso, F. González, G. González, L. A. González-Barrachina, P. Gracia, I. Gracia, J. M. Gras-Martí, A. Grecu, A. Habrard, A. Hazan, A. Hemming, J. Heredia-Agoiz, J. L. Herrero-Vicente, J. Higuera, C. De La Hontanilla, M. Iglesia-Vayá, M. Illescas, P. R. Iñesta, J. M. Izquierdo, E. Jakas, M. M. Jiménez, V. Jiménez, V. M. Jimeno-Morenilla, A. Jr., J. Hajic Kersten, S. Klapuri, A. Lartillot, O. Lemström, K. León, P. J. Ponce De León, P. J. Ponce León, Pedro J. Ponce Lidy, T. Llixiona, J. López, D. López-García, G. López-Gutiérrez, J. C. Lozano, M. Á. Madueño, A. Maestre, E. Manjón, J. V. Marín, R. Marsden, A. Martínez, M. T. Martínez-Álvarez, A. Martinez-Martin, E. Martínez-Sevilla, J. C. Mas-Candela, E. Mateiu, T. N. Mayer, R. Mazón, J. N. Mencarelli, A. Mico, L. Miotto, R. Molina-Carmona, A. J. R. Gallego Molina-Carmona, R. Mollineda, R. A. Montell, J. À. Morales, A. Moreno-Seco, F. Münnich, S. Nieto-Hidalgo, M. Nuñez-Alcover, Alicia Olivares-Rodríguez, C. Oncina, J. Orio, N. Orozco-Beltrán, D. Ortega-Bastida, J. Ortiz, A. Ortiz-Rojas, S. Pacha, A. Pascual, B. Pascual-León, N. Pérez, A. Pérez-Francisco, M. Pérez-García, Pérez-Sancho T. Pérez-García, T. Pérez-Herranz, F. M. Pérez-Ortiz, J. A. Perez-Sancho, C. Pertusa, A Pertusa, A. Piccoli, H. C. B. Pla, F. Pobil, A. P. Del Pomata, P. A. Pons, A. Pugin, L. Quesada, T. Quiles, J. A. Ramirez, R. Ramírez-Sánchez, G. Rauber, A. Recatalá, G. Regalado, Francisco José Castellanos Regimbal, J. Requena, A. Riba, P. Rico, J. R. Rico-Juan, J. R Rico-Juan, J. R. Ríos-Vila, A. Rizo, D. Rocamora, M. T. Rodríguez, R. Rojas, G. Roldán, J. M. Román, M. A. Rosello, A. Rulot, H. M. Saborit-Torres, J. M. Saenz-Gamboa, J. J. Sáez-Pérez, J. Salamon, J. Salas, F. Salinas, J. M. Salvador, C. Sanches, J. M. Sanches, J. Sánchez, J. A. Sánchez, J. S. Sánchez-Ferrer, A. Sánchez-Hernández, A. Sánchez-Martínez, F. Sanchiz, J. M. Sanz, P. J. Sanz, P. P. Sapp, C. S. Sarti, M. A. Scalco, M. Sebban, M. Segarra, E. Serra, X. Serrano, A. Serrano, J. F. Silla, C. N. Jr. Sober-Mira, J. Socorro, R. Sotoca, J. M. Sroubek, Z. Stefan, I. Suárez, A. Tardón, L. J. Thollard, F. Thomae, M. E. Toselli, A. H. Traver, V. J. Tuijl, B. Tylecek, R. Úbeda, J. I. Valero, S. Valero-Mas, J. J. Varo, M. A. Vayá, Saborit M. Iglesia Verdú-Mas, J. L. Vicente, O. Vidal, E. Viejo, D. Vigliensoni, G. Villegas, J. A. Vivas-Tejuelo, L. A. Yepes, J. A. Zaanen, M. Van All users David RizoJosé Manuel IñestaJorge Calvo-ZaragozaPedro José Ponce de LeónFrancisco J. CastellanosMaría AlfaroAntonio Ríos Vila

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115 entries « ‹ 3 of 3 › »

2016

Bosch, V.; Calvo-Zaragoza, J.; Toselli, A. H.; Vidal, E.

Sheet Music Statistical Layout Analysis Proceedings Article

In: 15th International Conference on Frontiers in Handwriting Recognition, 2016.

Links | BibTeX | Tags: TIMuL

@inproceedings{k351,

title = {Sheet Music Statistical Layout Analysis},

author = {V. Bosch and J. Calvo-Zaragoza and A. H. Toselli and E. Vidal},

url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/351/musicSheetLayout.pdf},

year  = {2016},

date = {2016-01-01},

booktitle = {15th International Conference on Frontiers in Handwriting Recognition},

keywords = {TIMuL},

pubstate = {published},

tppubtype = {inproceedings}

}

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• https://grfia.dlsi.ua.es/repositori/grfia/pubs/351/musicSheetLayout.pdf

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Calvo-Zaragoza, J.

Pattern Recognition for Music Notation PhD Thesis

2016.

Links | BibTeX | Tags:

@phdthesis{k343,

title = {Pattern Recognition for Music Notation},

author = {J. Calvo-Zaragoza},

editor = {Juan R. Rico-Juan and Jose Oncina},

url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/343/thesis-jcalvo.pdf},

year  = {2016},

date = {2016-01-01},

urldate = {2016-01-01},

organization = {Universidad de Alicante},

keywords = {},

pubstate = {published},

tppubtype = {phdthesis}

}

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• https://grfia.dlsi.ua.es/repositori/grfia/pubs/343/thesis-jcalvo.pdf

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2015

Calvo-Zaragoza, J.; Barbancho, I.; Tardón, L. J.; Barbancho, A. M.

Avoiding staff removal stage in optical music recognition: application to scores written in white mensural notation Journal Article

In: Pattern Analysis and Applications, vol. 18, no. 4, pp. 933-943, 2015, ISSN: 1433-7541.

Links | BibTeX | Tags: Prometeo 2012, TIMuL

@article{k318,

title = {Avoiding staff removal stage in optical music recognition: application to scores written in white mensural notation},

author = {J. Calvo-Zaragoza and I. Barbancho and L. J. Tardón and A. M. Barbancho},

url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/318/paaa-jcalvo.pdf},

issn = {1433-7541},

year  = {2015},

date = {2015-11-01},

urldate = {2015-11-01},

journal = {Pattern Analysis and Applications},

volume = {18},

number = {4},

pages = {933-943},

keywords = {Prometeo 2012, TIMuL},

pubstate = {published},

tppubtype = {article}

}

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• https://grfia.dlsi.ua.es/repositori/grfia/pubs/318/paaa-jcalvo.pdf

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Calvo-Zaragoza, J.; de León, P. J. Ponce; Iñesta, J. M.; Rizo, D.

Genre-based melody generation through multi-objective genetic algorithms Proceedings Article

In: Proceedings of the 8th Machine Learning and Music workshop (MML 2015), Vancouver (Canada), 2015.

Abstract | BibTeX | Tags: TIMuL

@inproceedings{k332,

title = {Genre-based melody generation through multi-objective genetic algorithms},

author = {J. Calvo-Zaragoza and P. J. Ponce de León and J. M. Iñesta and D. Rizo},

year  = {2015},

date = {2015-08-01},

urldate = {2015-08-01},

booktitle = {Proceedings of the 8th Machine Learning and Music workshop (MML 2015)},

address = {Vancouver (Canada)},

abstract = {Genetic-based composition algorithms have the ability to ex- plore an immense space of possibilities but the main di},

keywords = {TIMuL},

pubstate = {published},

tppubtype = {inproceedings}

}

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Genetic-based composition algorithms have the ability to ex- plore an immense space of possibilities but the main di

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Calvo-Zaragoza, J.; Valero-Mas, J. J.; Rico-Juan, J. R.

Prototype Generation on Structural Data using Dissimilarity Space Representation: A Case of Study Proceedings Article

In: Paredes, Roberto; Cardoso, Jaime S.; Pardo, Xosé M. (Ed.): 7th Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA), pp. 72-82, Springer, Santiago de Compostela, Spain, 2015, ISBN: 978-3-319-19389-2.

Abstract | Links | BibTeX | Tags: TIMuL

@inproceedings{k325,

title = {Prototype Generation on Structural Data using Dissimilarity Space Representation: A Case of Study},

author = {J. Calvo-Zaragoza and J. J. Valero-Mas and J. R. Rico-Juan},

editor = {Roberto Paredes and Jaime S. Cardoso and Xosé M. Pardo},

url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/325/prototype-generation-structural.pdf},

isbn = {978-3-319-19389-2},

year  = {2015},

date = {2015-06-01},

booktitle = {7th Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA)},

pages = {72-82},

publisher = {Springer},

address = {Santiago de Compostela, Spain},

abstract = {Data Reduction techniques are commonly applied in instance-based classification tasks to lower the amount of data to be processed. Prototype Selection (PS) and Prototype Generation (PG) constitute the most representative approaches. These two families differ in the way of obtaining the reduced set out of the initial one: while the former aims at selecting the most representative elements from the set, the latter creates new data out of it. Although PG is considered to better delimit decision boundaries, operations required are not so well defined in scenarios involving structural data such as strings, trees or graphs. 

This work proposes a case of study with the use of the common RandomC algorithm for mapping the initial structural data to a Dissimilarity Space (DS) representation, thereby allowing the use of PG methods. A comparative experiment over string data is carried out in which our proposal is faced to PS methods on the original space. Results show that PG combined with RandomC mapping achieves a very competitive performance, although the obtained accuracy seems to be bounded by the representativity of the DS method.},

keywords = {TIMuL},

pubstate = {published},

tppubtype = {inproceedings}

}

Close

Data Reduction techniques are commonly applied in instance-based classification tasks to lower the amount of data to be processed. Prototype Selection (PS) and Prototype Generation (PG) constitute the most representative approaches. These two families differ in the way of obtaining the reduced set out of the initial one: while the former aims at selecting the most representative elements from the set, the latter creates new data out of it. Although PG is considered to better delimit decision boundaries, operations required are not so well defined in scenarios involving structural data such as strings, trees or graphs.
This work proposes a case of study with the use of the common RandomC algorithm for mapping the initial structural data to a Dissimilarity Space (DS) representation, thereby allowing the use of PG methods. A comparative experiment over string data is carried out in which our proposal is faced to PS methods on the original space. Results show that PG combined with RandomC mapping achieves a very competitive performance, although the obtained accuracy seems to be bounded by the representativity of the DS method.

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• https://grfia.dlsi.ua.es/repositori/grfia/pubs/325/prototype-generation-structur[...]

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Calvo-Zaragoza, J.; Oncina, J.

Clustering of Strokes from Pen-based Music Notation: An Experimental Study Proceedings Article

In: Paredes, Roberto; Cardoso, Jaime S.; Pardo, Xosé M. (Ed.): 7th Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA), pp. 633-640, Springer, Santiago de Compostela, Spain, 2015, ISBN: 978-3-319-19389-2.

Links | BibTeX | Tags: Prometeo 2012, TIMuL

@inproceedings{k326,

title = {Clustering of Strokes from Pen-based Music Notation: An Experimental Study},

author = {J. Calvo-Zaragoza and J. Oncina},

editor = {Roberto Paredes and Jaime S. Cardoso and Xosé M. Pardo},

url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/326/clustering-strokes-pen.pdf},

isbn = {978-3-319-19389-2},

year  = {2015},

date = {2015-06-01},

booktitle = {7th Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA)},

pages = {633-640},

publisher = {Springer},

address = {Santiago de Compostela, Spain},

keywords = {Prometeo 2012, TIMuL},

pubstate = {published},

tppubtype = {inproceedings}

}

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• https://grfia.dlsi.ua.es/repositori/grfia/pubs/326/clustering-strokes-pen.pdf

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Calvo-Zaragoza, J.; Valero-Mas, J. J.; Rico-Juan, J. R

Improving kNN multi-label classification in Prototype Selection scenarios using class proposals Journal Article

In: Pattern Recognition, vol. 48, no. 5, pp. 1608-1622, 2015.

Links | BibTeX | Tags: Prometeo 2012, TIMuL

@article{k324,

title = {Improving kNN multi-label classification in Prototype Selection scenarios using class proposals},

author = {J. Calvo-Zaragoza and J. J. Valero-Mas and J. R Rico-Juan},

url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/324/improving-knn-multi.pdf},

year  = {2015},

date = {2015-01-01},

urldate = {2015-01-01},

journal = {Pattern Recognition},

volume = {48},

number = {5},

pages = {1608-1622},

keywords = {Prometeo 2012, TIMuL},

pubstate = {published},

tppubtype = {article}

}

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• https://grfia.dlsi.ua.es/repositori/grfia/pubs/324/improving-knn-multi.pdf

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Martínez-Álvarez, A.; Cuenca-Asensi, S.; Ortiz, A.; Calvo-Zaragoza, J.; Vivas-Tejuelo, L. A.

Tuning compilations by multi-objective optimization: Application to Apache web server Journal Article

In: Applied Soft Computing, vol. 29, pp. 461-470, 2015.

BibTeX | Tags:

@article{k327,

title = {Tuning compilations by multi-objective optimization: Application to Apache web server},

author = {A. Martínez-Álvarez and S. Cuenca-Asensi and A. Ortiz and J. Calvo-Zaragoza and L. A. Vivas-Tejuelo},

year  = {2015},

date = {2015-01-01},

urldate = {2015-01-01},

journal = {Applied Soft Computing},

volume = {29},

pages = {461-470},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

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Rico-Juan, J. R.; Calvo-Zaragoza, J.

Improving classification using a Confidence Matrix based on weak classifiers applied to OCR Journal Article

In: Neurocomputing, vol. 151, pp. 1354–1361, 2015, ISSN: 0925-2312.

Links | BibTeX | Tags: Prometeo 2012, TIMuL

@article{k323,

title = {Improving classification using a Confidence Matrix based on weak classifiers applied to OCR},

author = {J. R. Rico-Juan and J. Calvo-Zaragoza},

url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/323/cm.pdf},

issn = {0925-2312},

year  = {2015},

date = {2015-01-01},

urldate = {2015-01-01},

journal = {Neurocomputing},

volume = {151},

pages = {1354–1361},

keywords = {Prometeo 2012, TIMuL},

pubstate = {published},

tppubtype = {article}

}

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• https://grfia.dlsi.ua.es/repositori/grfia/pubs/323/cm.pdf

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2014

Calvo-Zaragoza, J.; Oncina, J.

Recognition of Pen-Based Music Notation: the HOMUS dataset Proceedings Article

In: Proceedings of the 22nd International Conference on Pattern Recognition, pp. 3038-3043, Stockholm, Sweden, 2014, ISBN: 978-1-4799-5208-3.

Links | BibTeX | Tags: Prometeo 2012

@inproceedings{k316,

title = {Recognition of Pen-Based Music Notation: the HOMUS dataset},

author = {J. Calvo-Zaragoza and J. Oncina},

url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/316/homus.pdf},

isbn = {978-1-4799-5208-3},

year  = {2014},

date = {2014-08-01},

urldate = {2014-08-01},

booktitle = {Proceedings of the 22nd International Conference on Pattern Recognition},

pages = {3038-3043},

address = {Stockholm, Sweden},

keywords = {Prometeo 2012},

pubstate = {published},

tppubtype = {inproceedings}

}

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• https://grfia.dlsi.ua.es/repositori/grfia/pubs/316/homus.pdf

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Calvo-Zaragoza, J.; Oncina, J.

Recognition of Pen-based Music Notation with Probabilistic Machines Proceedings Article

In: Proceedings of the 7th International Workshop on Machine Learning and Music, Barcelona, Spain, 2014.

BibTeX | Tags: TIMuL

@inproceedings{k330,

title = {Recognition of Pen-based Music Notation with Probabilistic Machines},

author = {J. Calvo-Zaragoza and J. Oncina},

year  = {2014},

date = {2014-01-01},

booktitle = {Proceedings of the 7th International Workshop on Machine Learning and Music},

address = {Barcelona, Spain},

keywords = {TIMuL},

pubstate = {published},

tppubtype = {inproceedings}

}

Close

Martínez-Álvarez, A.; Calvo-Zaragoza, J.; Cuenca-Asensi, S.; Ortiz, A.; Jimeno-Morenilla, A.

Multi-objective adaptive evolutionary strategy for tuning compilations Journal Article

In: Neurocomputing, vol. 123, pp. 381-389, 2014, ISSN: 0925-2312.

BibTeX | Tags:

@article{k309,

title = {Multi-objective adaptive evolutionary strategy for tuning compilations},

author = {A. Martínez-Álvarez and J. Calvo-Zaragoza and S. Cuenca-Asensi and A. Ortiz and A. Jimeno-Morenilla},

issn = {0925-2312},

year  = {2014},

date = {2014-01-01},

urldate = {2014-01-01},

journal = {Neurocomputing},

volume = {123},

pages = {381-389},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

Close

2013

Calvo-Zaragoza, J.; Oncina, J.

Human-Computer Interaction for Optical Music Recognition tasks Proceedings Article

In: Actas del III Workshop de Reconocimiento de Formas y Análisis de Imágenes, pp. 9-12, Madrid, Spain, 2013, ISBN: 978-84-695-8332-6.

Links | BibTeX | Tags: Prometeo 2012, TIASA

@inproceedings{k306,

title = {Human-Computer Interaction for Optical Music Recognition tasks},

author = {J. Calvo-Zaragoza and J. Oncina},

url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/306/wsrfai2013_submission_3.pdf},

isbn = {978-84-695-8332-6},

year  = {2013},

date = {2013-09-01},

urldate = {2013-09-01},

booktitle = {Actas del III Workshop de Reconocimiento de Formas y Análisis de Imágenes},

pages = {9-12},

address = {Madrid, Spain},

keywords = {Prometeo 2012, TIASA},

pubstate = {published},

tppubtype = {inproceedings}

}

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• https://grfia.dlsi.ua.es/repositori/grfia/pubs/306/wsrfai2013_submission_3.pdf

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Calvo-Zaragoza, J.; Oncina, J.; Iñesta, J. M.

Recognition of Online Handwritten Music Symbols Proceedings Article

In: Proceedings of the 6th International Workshop on Machine Learning and Music, Prague, Czech Republic, 2013.

Abstract | Links | BibTeX | Tags: Prometeo 2012, TIASA

@inproceedings{k307,

title = {Recognition of Online Handwritten Music Symbols},

author = {J. Calvo-Zaragoza and J. Oncina and J. M. Iñesta},

url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/307/calvozaragoza-mml13.pdf},

year  = {2013},

date = {2013-01-01},

urldate = {2013-01-01},

booktitle = {Proceedings of the 6th International Workshop on Machine Learning and Music},

address = {Prague, Czech Republic},

abstract = {An effective way of digitizing a new musical composition is to use an e-pen and tablet application in which the user's pen strokes are recognized online and the digital score is created with the sole effort of the composition itself. This work aims to be a starting point for research on the recognition of online handwritten music notation. To this end, different alternatives within the two modalities of recognition resulting from this data are presented: online recognition, which uses the strokes marked by a pen, and offline recognition, which uses the image generated after drawing the symbol. A comparative experiment with common machine learning algorithms over a dataset of 3800 samples and 32 different music symbols is presented. Results show that samples of the actual user are needed if good classification rates are pursued. Moreover, algorithms using the online data, on average, achieve better classifocation results than the others.},

keywords = {Prometeo 2012, TIASA},

pubstate = {published},

tppubtype = {inproceedings}

}

Close

An effective way of digitizing a new musical composition is to use an e-pen and tablet application in which the user's pen strokes are recognized online and the digital score is created with the sole effort of the composition itself. This work aims to be a starting point for research on the recognition of online handwritten music notation. To this end, different alternatives within the two modalities of recognition resulting from this data are presented: online recognition, which uses the strokes marked by a pen, and offline recognition, which uses the image generated after drawing the symbol. A comparative experiment with common machine learning algorithms over a dataset of 3800 samples and 32 different music symbols is presented. Results show that samples of the actual user are needed if good classification rates are pursued. Moreover, algorithms using the online data, on average, achieve better classifocation results than the others.

Close

• https://grfia.dlsi.ua.es/repositori/grfia/pubs/307/calvozaragoza-mml13.pdf

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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}

}

Close

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.

Close

• https://grfia.dlsi.ua.es/repositori/grfia/pubs/265/ibpria11-calvo.pdf

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115 entries « ‹ 3 of 3 › »