Evolutionary Feature Selection for Spiking Neural Network Pattern Classifiers - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2005

Evolutionary Feature Selection for Spiking Neural Network Pattern Classifiers

Abstract

This paper presents an application of the biologically realistic JASTAP neural network model to classification tasks. The JASTAP neural network model is presented as an alternative to the basic multi-layer perceptron model. An evolutionary procedure previously applied to the simultaneous solution of feature selection and neural network training on standard multi-layer perceptrons is extended with JASTAP model. Preliminary results on IRIS standard data set give evidence that this extension allows the use of smaller neural networks that can handle noisier data without any degradation in classification accuracy.
Fichier principal
Vignette du fichier
VMC05.pdf (125.14 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-00643498 , version 1 (22-11-2011)

Identifiers

Cite

Michal Valko, Nuno Cavalheiro, Marco Castelani. Evolutionary Feature Selection for Spiking Neural Network Pattern Classifiers. Proceedings of 2005 Portuguese Conference on Artificial Intelligence, Dec 2005, Covilha, Portugal. pp.181-187, ⟨10.1109/EPIA.2005.341291⟩. ⟨hal-00643498⟩
77 View
284 Download

Altmetric

Share

Gmail Facebook X LinkedIn More