Método de conjuntos de nivel para máquinas de soporte vectorial (2020)
We propose an empirical sampling procedure to solve the problem of support vector machines in large datasets. The general idea is to take a greater number of samples close to regions where, in distribution, a greater number of support vectors are found. This approach has a lower computational cost than getting the exact solution.