RT Journal Article
T1 Improving learning and generalization capabilities of the C-Mantec constructive neural network algorithm.
A1 Gómez-Gallego, Iván
A1 Mesa, Héctor
A1 Ortega-Zamorano, Francisco
A1 Jerez-Aragonés, José Manuel
A1 Franco, Leónardo
K1 Redes neuronales (Informática)
K1 Aprendizaje automático (Inteligencia artificial)
K1 Proceso de vectores (Informática)
AB C-Mantec neural network constructive algorithm Ortega (C-Mantec neural network algorithm implementation on MATLAB. https://github.com/IvanGGomez/CmantecPaco, 2015) creates very compact architectures with generalization capabilities similar to feed-forward networks trained by the well-known back-propagation algorithm. Nevertheless, constructive algorithms suffer much from the problem of overfitting, and thus, in this work the learning procedure is first analyzed for networks created by this algorithm with the aim of trying to understand the training dynamics that will permit optimization possibilities. Secondly, several optimization strategies are analyzed for the position of class separating hyperplanes, and the results analyzed on a set of public domain benchmark data sets. The results indicate that with these modifications a small increase in prediction accuracy of C-Mantec can be obtained but in general this was not better when compared to a standard support vector machine, except in some cases when a mixed strategy is used.
PB Springer
YR 2019
FD 2019-08-01
LK https://hdl.handle.net/10630/40267
UL https://hdl.handle.net/10630/40267
LA eng
NO Gómez, I., Mesa, H., Ortega-Zamorano, F. et al. Improving learning and generalization capabilities of the C-Mantec constructive neural network algorithm. Neural Comput & Applic 32, 8955–8963 (2020).
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 19 ene 2026