Learning discriminative tree edit similarities for linear classification — Application to melody recognition
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http://hdl.handle.net/10045/61928
Título: | Learning discriminative tree edit similarities for linear classification — Application to melody recognition |
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Autor/es: | Bellet, Aurélien | Bernabeu Briones, José Francisco | Habrard, Amaury | Sebban, Marc |
Centro, Departamento o Servicio: | Universidad de Alicante. Instituto Universitario de Investigación Informática |
Palabras clave: | Edit distance | Convex optimization | Tree-structured data | Melody recognition |
Área/s de conocimiento: | Lenguajes y Sistemas Informáticos |
Fecha de publicación: | 19-nov-2016 |
Editor: | Elsevier |
Cita bibliográfica: | Neurocomputing. 2016, 214: 155-161. doi:10.1016/j.neucom.2016.06.006 |
Resumen: | Similarity functions are a fundamental component of many learning algorithms. When dealing with string or tree-structured data, measures based on the edit distance are widely used, and there exist a few methods for learning them from data. In this context, we recently proposed GESL (Bellet et al., 2012 [3]), an approach to string edit similarity learning based on loss minimization which offers theoretical guarantees as to the generalization ability and discriminative power of the learned similarities. In this paper, we argue that GESL, which has been originally dedicated to deal with strings, can be extended to trees and lead to powerful and competitive similarities. We illustrate this claim on a music recognition task, namely melody classification, where each piece is represented as a tree modeling its structure as well as rhythm and pitch information. The results show that GESL outperforms standard as well as probabilistically-learned edit distances and that it is able to describe consistently the underlying melodic similarity model. |
Patrocinador/es: | This work was supported by a grant from CPER Nord-Pas de Calais/FEDER DATA Advanced data science and technologies 2015-2020 and the Spanish Ministerio de Economía y Competitividad project TIMuL (No. TIN2013--48152--C2--1--R supported by UE FEDER funds). |
URI: | http://hdl.handle.net/10045/61928 |
ISSN: | 0925-2312 (Print) | 1872-8286 (Online) |
DOI: | 10.1016/j.neucom.2016.06.006 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © 2016 Elsevier B.V. |
Revisión científica: | si |
Versión del editor: | http://dx.doi.org/10.1016/j.neucom.2016.06.006 |
Aparece en las colecciones: | Personal Investigador sin Adscripción a Grupo |
Archivos en este ítem:
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2016_Bellet_etal_Neurocomputing_final.pdf | Versión final (acceso restringido) | 580,1 kB | Adobe PDF | Abrir Solicitar una copia |
2016_Bellet_etal_Neurocomputing_preprint.pdf | Preprint (acceso abierto) | 598,53 kB | Adobe PDF | Abrir Vista previa |
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