Abstract
The development of intelligents teaching-learning systems depends, on one hand, of the pedagogical paradigms and, on the other hand, of the available technologies to implement these paradigms in computers. The field of the Intelligent Teaching-Learning Systems is characterized by the application of Artificial Intelligence techniques, to the development of the teaching-learning process assisted by computers, where the term "intelligent" is associated to the student’s aptitude to dynamically acclimatize to the teaching process by carrying out an individual learning. The case-based reasoning is an Artificial Intelligence technique that performs their reasoning process based on previously solved cases, stored in case-bases. In this article we propose a new case-based approach with foundations on fuzzy pattern recognition to help elaborate intelligents teaching-learning systems, using the FS-testor theory, based on a combination of typical testor theory with the fuzzy sets, assures the efficient access and retrieval of cases.
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References
García, Z.: Investigación y Elaboración de Sistemas de Enseñanza Inteligentes. Universidad Central de Las Villas, Santa Clara, Cuba (1993)
Gutiérrez, I., Bello, R.: Determination and Handling of Uncertainty in Case-Base Systems. In: Proceedings of the 7th Joint International Iberoamerican Conference. Conference on Artificial Intelligence, 15th Brazilian Conference on AI (2000)
Gutierrez, I., Bello, R.: A decision case-based system that reasons in uncertainty conditions. Springer, Heidelberg (2002)
Lazo Cortés, M.: Una generalización del concepto de testor, Aportaciones Matemáticas, Serie comunicaciones 14, IMATE-UNAM (1994)
Shulcloper, J.: Introducción al Reconocimiento de Patrones (Enfoque Lógico-Combinatorio). Serie Verde No. 51, CINVESTAV-IPN, México (1995)
Alba, E.: El concepto de FS-testor: una solución para un problema de incompatibilidad. Revista Ciencias Matemáticas (2002)
Didier, D.: The three semantics of fuzzy sets. Fuzzy Set and Systems (1997)
Bareiss, D.: Exemplar-based knowledge acquisition: A unified approach to concept representation, classification, and learning. Academic Press, Boston (1989)
Pons, A.: Lex: un nuevo algoritmo para el cálculo de los testores típicos. Revista ciencias matemáticas 21(1) (2003)
Hatzilygeroudis, I.: An Expert System with Certainty Factors for Predicting Student Success. Springer, Heidelberg (2004)
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Martínez, N., León, M., García, Z. (2007). Features Selection Through FS-Testors in Case-Based Systems of Teaching-Learning. In: Gelbukh, A., Kuri Morales, Á.F. (eds) MICAI 2007: Advances in Artificial Intelligence. MICAI 2007. Lecture Notes in Computer Science(), vol 4827. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76631-5_115
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DOI: https://doi.org/10.1007/978-3-540-76631-5_115
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