Regularization of Extreme Learning Machines with information of spatial relations of the projected data
Lourenço R. G. Araújo, Luiz C. B. Torres, Leonardo J. Silvestre, Carla Caldeira Takahashi, Antônio P. Braga · 2019
The following work presents a new approach to automatic selection of Tikhonov's regularization parameter, responsible for controlling the weight value of an ELM neural network. A strategy based on measurements obtained from data projection (Fisher-Score) is introduced. Seven datasets are tested and results are compared to those obtained when the regularization parameter is selected through cross-validation. The strategy shows satisfactory classification performance (in terms of p-value), while presenting significant training time reduction.