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Evolutionary feature selection for spiking neural network pattern classifiers — arXiv:2604.26654

Source: arXiv
Original: https://arxiv.org/abs/2604.26654
License (as archived): CC0-1.0
Archived: 2026-10-06T05:31:51.507Z
SHA-256 of the archived text: ad5aa541f5f556658b9eb7fc1a7e4bc1b6a5ae5b7905f1b53dc6cb3a4c729b93
Stable URL of this page: https://metraipolis.com/documento/dc_2d1feaffbdbf

How to cite

Evolutionary feature selection for spiking neural network pattern classifiers — arXiv:2604.26654 — arXiv, https://arxiv.org/abs/2604.26654 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_2d1feaffbdbf (Metraipolis archive dc_2d1feaffbdbf, sha256 ad5aa541f5f556658b9eb7fc1a7e4bc1b6a5ae5b7905f1b53dc6cb3a4c729b93)

BibTeX

@misc{dc2d1feaffbdbf,
  title = {Evolutionary feature selection for spiking neural network pattern classifiers — arXiv:2604.26654},
  howpublished = {arXiv},
  url = {https://arxiv.org/abs/2604.26654},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_2d1feaffbdbf; sha256 ad5aa541f5f556658b9eb7fc1a7e4bc1b6a5ae5b7905f1b53dc6cb3a4c729b93},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv, https://arxiv.org/abs/2604.26654. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.LG, nuovi lavori del giorno).
Categorie arXiv: Neural and Evolutionary Computing (cs.NE) ; Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2604.26654 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-06T05:27:05.921Z dalla pagina https://arxiv.org/list/cs.LG/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).