Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/114745
Título: Piecewise polynomial activation functions for feedforward neural networks
Autores/as: López-Rubio, Ezequiel
Ortega Zamorano, Francisco 
Domínguez, Enrique
Muñoz-Pérez, José
Clasificación UNESCO: 1203 Ciencia de los ordenadores
Palabras clave: Activation functions
Feedforward neural networks
Supervised learning
Regression
Classification
Fecha de publicación: 2019
Publicación seriada: Neural Processing Letters 
Resumen: Since the origins of artificial neural network research, many models of feedforward networks have been proposed. This paper presents an algorithm which adapts the shape of the activation function to the training data, so that it is learned along with the connection weights. The activation function is interpreted as a piecewise polynomial approximation to the distribution function of the argument of the activation function. An online learning procedure is given, and it is formally proved that it makes the training error decrease or stay the same except for extreme cases. Moreover, the model is computationally simpler than standard feedforward networks, so that it is suitable for implementation on FPGAs and microcontrollers. However, our present proposal is limited to two-layer, one-output-neuron architectures due to the lack of differentiability of the learned activation functions with respect to the node locations. Experimental results are provided, which show the performance of the proposal algorithm for classification and regression applications.
URI: http://hdl.handle.net/10553/114745
ISSN: 1370-4621
DOI: 10.1007/s11063-018-09974-4
Fuente: Neural Processing Letters [ISSN 1370-4621], n. 50, p. 121-147
Colección:Artículos
Vista completa

Citas SCOPUSTM   

6
actualizado el 24-mar-2024

Visitas

65
actualizado el 16-mar-2024

Google ScholarTM

Verifica

Altmetric


Comparte



Exporta metadatos



Los elementos en ULPGC accedaCRIS están protegidos por derechos de autor con todos los derechos reservados, a menos que se indique lo contrario.