Advances in feedforward neural networks : Demystifying knowledge acquiring black boxes
- Author
- LOONEY, C. G1
[1] Computer Science Department, University of Nevada, Reno, NV 89557, United States - Source
IEEE transactions on knowledge and data engineering. 1996, Vol 8, Num 2, pp 211-226 ; ref : 76 ref
- ISSN
- 1041-4347
- Scientific domain
- Control theory, operational research; Computer science; Psychology, psychopathology, psychiatry
- Publisher
- IEEE Computer Society, New York, NY
- Publication country
- United States
- Document type
- Article
- Language
- English
- Keyword (fr)
- Apprentissage Boucle anticipation Méthode adaptative Méthode gradient Reconnaissance forme Réseau neuronal Rétropropagation Saisie donnée Black boxe model Multilayered perceptron Training
- Keyword (en)
- Learning Feedforward Adaptive method Gradient method Pattern recognition Neural network Backpropagation Data acquisition
- Keyword (es)
- Aprendizaje Ciclo anticipación Método adaptativo Método gradiente Reconocimiento patrón Red neuronal Retropropagacíon Toma dato
- Classification
- Pascal
- 001 Exact sciences and technology / 001D Applied sciences / 001D02 Computer science; control theory; systems / 001D02B Software / 001D02B07 Memory organisation. Data processing / 001D02B07D Information systems. Data bases
- 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
- 3084001
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
10.1109/69.494162 
https://dx.doi.org/10.1109/69.494162
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