RT Journal Article
T1 A critical analysis of the theoretical framework of the Extreme Learning Machine.
A1 Perfilieva, Irina
A1 Madrid-Labrador, Nicolás Miguel
A1 Ojeda-Aciego, Manuel
A1 Artiemjew, Piotr
A1 Niemczynowicz, Agnieszka
K1 Aprendizaje automático (Inteligencia artificial)
K1 Redes neuronales (Informática)
K1 Matrices (Matemáticas)
AB Despite several successful applications of the Extreme Learning Machine (ELM) as a new neural network training method that combines random selection with deterministic computation, we show that some fundamental principles of ELM lack a rigorous mathematical basis. In particular, we refute the proofs of two fundamental claims and construct datasets that serve as counterexamples to the ELM algorithm. Finally, we provide alternative claims to the basic principles that justify the effectiveness of ELM in some theoretical cases.
PB Elsevier
YR 2025
FD 2025-01-02
LK https://hdl.handle.net/10630/40247
UL https://hdl.handle.net/10630/40247
LA eng
NO Irina Perfilieva, Nicolás Madrid, Manuel Ojeda-Aciego, Piotr Artiemjew, Agnieszka Niemczynowicz: A critical analysis of the theoretical framework of the Extreme Learning Machine. Neurocomputing 621: 129298 (2025)
NO https://openpolicyfinder.jisc.ac.uk/id/publication/15862
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 19 ene 2026