| Título |
Comparing Deep and Dendrite Neural Networks: A Case Study |
| Tipo |
Congreso |
| Sub-tipo |
Memoria |
| Descripción |
19th Mexican Conference on Pattern Recognition, MCPR 2017 |
| Resumen |
In this paper, a comparative study between two different neural network models is performed for a very simple type of classificaction problem in 2D. The first model is a deep neural network and the second is a dendrite morphological neuron. The metrics to be compared are: training time, classification accuracies and number of learning parameters. We also compare the decision boundaries generated by both models. The experiments show that the dendrite morphological neurons surpass the deep neural networks by a wide margin in terms of higher accuracies and a lesser number of parameters. From this, we raise the hypothesis that deep learning networks can be improved adding morphological neurons. |
| Observaciones |
DOI: 10.1007/978-3-319-59226-8_4
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 10267 |
| Lugar |
Huatulco |
| País |
Mexico |
| No. de páginas |
32-41 |
| Vol. / Cap. |
10267 LNCS |
| Inicio |
2017-06-21 |
| Fin |
2017-06-24 |
| ISBN/ISSN |
9783319592251 |
