681 citations · 772 across the 8 of their papers we have counts for
3 papers · 1 filter
A robust approach for deep neural networks in presence of label noise: relabelling and filtering instances during training
Anabel Gómez-Ríos, Julián Luengo, Francisco Herrera
Deep learning has outperformed other machine learning algorithms in a variety of tasks, and as a result, it is widely used. However, like other machine learning algorithms, deep le…
EXplainable Neural-Symbolic Learning (X-NeSyL) methodology to fuse deep learning representations with expert knowledge graphs: the MonuMAI cultural heritage use case
Natalia Díaz-Rodríguez, Alberto Lamas, Jules Sanchez +7
The latest Deep Learning (DL) models for detection and classification have achieved an unprecedented performance over classical machine learning algorithms. However, DL models are…
MNIST-NET10: A heterogeneous deep networks fusion based on the degree of certainty to reach 0.1 error rate. Ensembles overview and proposal
S. Tabik, R. F. Alvear-Sandoval, M. M. Ruiz +3
Ensemble methods have been widely used for improving the results of the best single classificationmodel. A large body of works have achieved better performance mainly by applying o…