RT Journal Article
T1 The forbidden region self-organizing map neural network
A1 Díaz-Ramos, Antonio
A1 López-Rubio, Ezequiel
A1 Palomo-Ferrer, Esteban José
K1 Informática
K1 Redes neuronales (Informática)
AB Self-organizing maps are aimed to learn a representation of the input distribution which faithfully describes the topological relations among the clusters of the distribution. For some datasets and applications, it is known beforehand that some regions of the input space cannot contain any samples. Those areknown as forbidden regions. In these cases, any prototype which lies in a forbidden region is meaningless. However, previous self-organizing models do not address this problem. In this work we propose a new self-organizing map model which is guaranteed to keep all prototypes out of a set of prespecified forbidden regions. Experimental results are reported, which show that our proposal outperforms the SOM both in terms of vector quantization error and quality of the learned topological maps.
YR 2020
FD 2020
LK https://hdl.handle.net/10630/32962
UL https://hdl.handle.net/10630/32962
LA eng
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 24 ene 2026