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11 results on '"Fombellida, Juan"'

3. Artificial Metaplasticity: Application to MIT-BIH Arrhythmias Database

Author

Torres-Alegre, Santiago, Fombellida, Juan, Piñuela-Izquierdo, Juan Antonio, Andina, Diego, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Ferrández Vicente, José Manuel, editor, Álvarez-Sánchez, José Ramón, editor, de la Paz López, Félix, editor, Toledo-Moreo, Fco. Javier, editor, and Adeli, Hojjat, editor

Published
2015
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4. Artificial Metaplasticity for Deep Learning: Application to WBCD Breast Cancer Database Classification

Author

Fombellida, Juan, Torres-Alegre, Santiago, Piñuela-Izquierdo, Juan Antonio, Andina, Diego, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Ferrández Vicente, José Manuel, editor, Álvarez-Sánchez, José Ramón, editor, de la Paz López, Félix, editor, Toledo-Moreo, Fco. Javier, editor, and Adeli, Hojjat, editor

Published
2015
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11. ARTIFICIAL METAPLASTICITY CAN IMPROVE ARTIFICIAL NEURAL NETWORKS LEARNING.

Author

Andina, Diego, Álvarez-Vellisco, Antonio, Jevtic, Aleksandar, and Fombellida, Juan

Subjects
PERCEPTRONS, ARTIFICIAL neural networks, SELF-organizing systems, BACK propagation, ARTIFICIAL intelligence, MACHINE learning, EVOLUTIONARY computation, SPEECH perception
Abstract

Metaplasticity property of biological synapses is interpreted in this paper as the concept of placing greater emphasis on training patterns that are less frequent. A novel implementation is proposed in which, during the network learning phase, a priority is given to weight updating of less frequent activations over the more frequent ones. Modeling this interpretation in the training phase, the hypothesis of an improved training is tested on the Multilayer Perceptron type network with Backpropagation training. The results obtained for the chosen application show a much more efficient training, while at least maintaining the Multilayer Perceptron performance. [ABSTRACT FROM AUTHOR]

Published
2009

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