Ponencia
A Quasi-Metric for Machine Learning
Autor/es | 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 de publicación | 2002 |
Fecha de depósito | 2018-03-27 |
Publicado en |
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ISBN/ISSN | 978-3-540-00131-7 0302-9743 |
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 ... 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. |
Agencias financiadoras | Ministerio de Ciencia y Tecnología (MCYT). España Junta de Andalucía |
Identificador del proyecto | TIC 2000-1368-C03-0
TIC-137 |
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. |
Ficheros | Tamaño | Formato | Ver | Descripción |
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A Quasi-Metric (1).pdf | 383.2Kb | [PDF] | Ver/ | |