An efficient self-organizing map (E-SOM) learning algorithm using group of neurons
- Author
- CHAUDHARY, Vikas1 ; BHATIA, R. S1 ; AHLAWAT, Anil K2
[1] National Institute of Technology (N.I.T.), Kurukshetra, Haryana, India
[2] Krishna Institute of Engineering & Technology, Ghaziabad, U.P., India - Source
International journal of computational intelligence systems. 2014, Vol 7, Num 1-6, pp 963-972, 10 p ; ref : 27 ref
- ISSNNV
- 1875-6891
- Scientific domain
- Cognition; Computer science
- Publisher
- Taylor & Francis, Amsterdam
- Publication country
- Netherlands
- Document type
- Article
- Language
- English
- Author keyword
- Efficient SOM (E-SOM) Self-organizing map (SOM) distant neuron kernel function
- Keyword (fr)
- Algorithme Kohonen Algorithme apprentissage Autoorganisation Efficacité Estimation erreur Fonction noyau Intelligence artificielle Réseau neuronal Résultat expérimental Topologie
- Keyword (en)
- Kohonen algorithm Learning algorithm Self organization Efficiency Error estimation Kernel function Artificial intelligence Neural network Experimental result Topology
- Keyword (es)
- Algoritmo Kohonen Algoritmo aprendizaje Autoorganización Eficacia Estimación error Función núcleo Inteligencia artificial Red neuronal Resultado experimental Topología
- Classification
- Pascal
- 001 Exact sciences and technology / 001D Applied sciences / 001D02 Computer science; control theory; systems / 001D02C Artificial intelligence / 001D02C06 Connectionism. Neural networks
- Discipline
- Computer science : theoretical automation and systems
- Origin
- Inist-CNRS
- Database
- PASCAL
- INIST identifier
- 29014266
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
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DOI
10.1080/18756891.2014.966995 
https://dx.doi.org/10.1080/18756891.2014.966995
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