English   español  
Por favor, use este identificador para citar o enlazar a este item: http://hdl.handle.net/10261/130281
Compartir / Impacto:
Estadísticas
Add this article to your Mendeley library MendeleyBASE
Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL
Título

Speeding up operations on feature terms using constraint programming and variable symmetry

AutorOntañón, Santiago; Meseguer, Pedro
Palabras claveNatural language processing systems
Learning systems
Constraint programing
Variable symmetries
Inductive logic programming
Constraint theory
Learning algorithms
Fecha de publicación2015
EditorElsevier
CitaciónArtificial Intelligence 220: 104- 120 (2015)
ResumenFeature terms are a generalization of first-order terms which have recently received increased attention for their usefulness in structured machine learning, natural language processing and other artificial intelligence applications. One of the main obstacles for their wide usage is that, when set-valued features are allowed, their basic operations (subsumption, unification, and antiunification) have a very high computational cost. We present a Constraint Programming formulation of these operations, which in some cases provides orders of magnitude speed-ups with respect to the standard approaches. In addition, exploiting several symmetries - that often appear in feature terms databases - causes substantial additional savings. We provide experimental results of the benefits of this approach. © 2014 Elsevier B.V. All rights reserved.
URIhttp://hdl.handle.net/10261/130281
DOI10.1016/j.artint.2014.11.010
Identificadoresdoi: 10.1016/j.artint.2014.11.010
issn: 0004-3702
Aparece en las colecciones: (IIIA) Artículos
Ficheros en este ítem:
Fichero Descripción Tamaño Formato  
accesoRestringido.pdf15,38 kBAdobe PDFVista previa
Visualizar/Abrir
Mostrar el registro completo
 

Artículos relacionados:


NOTA: Los ítems de Digital.CSIC están protegidos por copyright, con todos los derechos reservados, a menos que se indique lo contrario.