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pro vyhledávání: '"Ventura, Sebastián"'
Data stream learning is a very relevant paradigm because of the increasing real-world scenarios generating data at high velocities and in unbounded sequences. Stream learning aims at developing models that can process instances as they arrive, so mod
Externí odkaz:
http://arxiv.org/abs/2410.20242
Publikováno v:
Engineering Applications of Artificial Intelligence, Volume 81, May 2019, Pages 14-28
Although metaheuristics have been widely recognized as efficient techniques to solve real-world optimization problems, implementing them from scratch remains difficult for domain-specific experts without programming skills. In this scenario, metaheur
Externí odkaz:
http://arxiv.org/abs/2402.18616
Publikováno v:
Information Sciences, vol. 463-464, pp. 92-109, 2018
While working on a software specification, designers usually need to evaluate different architectural alternatives to be sure that quality criteria are met. Even when these quality aspects could be expressed in terms of multiple software metrics, oth
Externí odkaz:
http://arxiv.org/abs/2401.04192
In this paper we explore capabilities of spiking neural networks in solving multi-task classification problems using the approach of single-tasking of multiple tasks. We designed and implemented a multi-task spiking neural network (MT-SNN) that can l
Externí odkaz:
http://arxiv.org/abs/2208.01522
Publikováno v:
In Information Sciences September 2024 678
Publikováno v:
In Swarm and Evolutionary Computation December 2024 91
Autor:
Muñoz, Emanuel G., Parraga-Alava, Jorge, Meza, Jaime, Proaño Morales, Jonathan Josue, Ventura, Sebastian
Publikováno v:
In Heliyon 15 March 2024 10(5)
Autor:
Ávila-Jiménez, J.L., Cantón-Habas, Vanesa, Carrera-González, María del Pilar, Rich-Ruiz, Manuel, Ventura, Sebastián
Publikováno v:
In Computers in Biology and Medicine February 2024 169
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