RT Conference Proceedings
T1 Selecting Algorithms by Using ATD
A1 Encina Vara, Alberto De La
A1 Hidalgo Herrero, Mercedes
A1 Rubio Díez, Fernando
AB 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 similaritiesand differences between both of them.
SN 978-1-5386-6650-0
SN 2577-1655
YR 2018
FD 2018
LK https://hdl.handle.net/20.500.14352/88643
UL https://hdl.handle.net/20.500.14352/88643
LA eng
NO 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.
DS Docta Complutense
RD 5 abr 2025