Opened Access A Quasi-Metric for Machine Learning

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Autor: Gutiérrez Naranjo, Miguel Ángel
Alonso Jiménez, José Antonio
Borrego Díaz, Joaquín
Departamento: Universidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificial
Fecha: 2002
Publicado en: IBERAMIA 2002: 8th Ibero-American Conference on Artificial Intelligence (2002), p 193-203
ISBN/ISSN: 978-3-540-00131-7
0302-9743
Tipo de documento: Ponencia
Resumen: The subsumption relation is crucial in the Machine Learning systems based on a clausal representation. In this paper we present a class of operators for Machine Learning based on clauses which is a characterization of the subsumption relation in the following sense: The clause C 1 subsumes the clause C 2 iff C 1 can be reached from C 2 by applying these operators. In the second part of the paper we give a formalization of the closeness among clauses based on these operators and an algorithm to compute it as well as a bound for a quick estimation.
Cita: Gutiérrez Naranjo, M.Á., Alonso Jiménez, J.A. y Borrego Díaz, J. (2002). A Quasi-Metric for Machine Learning. En IBERAMIA 2002: 8th Ibero-American Conference on Artificial Intelligence (193-203), Seville, Spain: Springer.
Tamaño: 383.2Kb
Formato: PDF

URI: https://hdl.handle.net/11441/71334

DOI: 10.1007/3-540-36131-6_20

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