2026-06-27T16:05:33Zhttps://docta.ucm.es/rest/oai/requestoai:docta.ucm.es:20.500.14352/886432025-03-26T00:55:59Zcom_20.500.14352_14col_20.500.14352_15
Docta Complutense
author
Encina Vara, Alberto De La
author
Hidalgo Herrero, Mercedes
author
Rubio Díez, Fernando
2023-11-08T14:56:10Z
2023-11-08T14:56:10Z
2018
M. Hidalgo-Herrero, A. De La Encina and F. Rubio, "Selecting Algorithms by Using ATD," 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Miyazaki, Japan, 2018, pp. 11-16, doi: 10.1109/SMC.2018.00011.
978-1-5386-6650-0
2577-1655
10.1109/SMC.2018.00011
https://hdl.handle.net/20.500.14352/88643
https://doi.org/10.1109/SMC.2018.00011
The Anthropological Theory of the Didactic (ATD) has proven its usefulness to describe human learning, and has improved the methods used to acquire new knowledge. However, although the same framework could be used in the context of machine learning, no attempts have appeared in the literature to apply ATD within computing environments. In this paper we argue that we can also take profit from ATD in machine learning. In particular, we will show how we can use it to describe a simple system where we need to learn to select an algorithm among a set of predefined options. The approach will be used to analyze both human learning and machine learning, studying the similarities
and differences between both of them.
eng
Attribution-NonCommercial-NoDerivatives 4.0 International
Selecting Algorithms by Using ATD
conference paper
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Selecting_Algorithms.pdf