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ORCID: https://orcid.org/0000-0003-0652-9872, Sierra Araujo, Basilio, Etxeberria Iriondo, Ramón, Lozano Alonso, José Antonio and Peña Sánchez, José María
ORCID: https://orcid.org/0000-0001-9123-1020
(1999).
Representing the behaviour of supervised classification learning algorithms by Bayesian networks.
"Pattern Recognition Letters", v. 20
(n. 11-13);
pp. 1201-1209.
ISSN 0167-8655.
https://doi.org/10.1016/S0167-8655(99)00095-1.
| Título: | Representing the behaviour of supervised classification learning algorithms by Bayesian networks |
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| Autor/es: |
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| Tipo de Documento: | Artículo |
| Título de Revista/Publicación: | Pattern Recognition Letters |
| Fecha: | Noviembre 1999 |
| ISSN: | 0167-8655 |
| Volumen: | 20 |
| Número: | 11-13 |
| Materias: | |
| ODS: | |
| Palabras Clave Informales: | Machine Learning, Classification learning algorithm, Joint behaviour, Bayesian networks, Structure learning |
| Escuela: | Facultad de Informática (UPM) [antigua denominación] |
| Departamento: | Inteligencia Artificial |
| Licencias Creative Commons: | Reconocimiento - Sin obra derivada - No comercial |
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In this paper, an approach to study the nature of the classification models induced by Machine Learning algorithms is proposed. Instead of the predictive accuracy, the values of the predicted class labels are used to characterize the classification models. Over these predicted class labels Bayesian networks are induced. Using these Bayesian networks, several assertions are extracted about the nature of the classification models induced by Machine Learning algorithms.
| ID de Registro: | 73484 |
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| Identificador DC: | https://oa.upm.es/73484/ |
| Identificador OAI: | oai:oa.upm.es:73484 |
| URL Portal Científico: | https://portalcientifico.upm.es/es/ipublic/item/5477950 |
| Identificador DOI: | 10.1016/S0167-8655(99)00095-1 |
| URL Oficial: | https://www.sciencedirect.com/science/article/pii/... |
| Depositado por: | Biblioteca Facultad de Informatica |
| Depositado el: | 05 May 2023 07:32 |
| Ultima Modificación: | 08 Jun 2026 07:57 |
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