Binary Factorization by Neural Autoassociator.
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F03%3A00008367" target="_blank" >RIV/61989100:27240/03:00008367 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Binary Factorization by Neural Autoassociator.
Original language description
He unsupervised learning of feature extraction in high-dimensional patterns is a central problem for neural network approach. Feature extraction is the procedure which maps original patterns into the features (or factors) space of reduced dimension. In this paper we demonstrate that Hebbian learning in Hopfield-like neural network is a natural procedure for unsupervised learning of feature extraction.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
JC - Computer hardware and software
OECD FORD branch
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Result continuities
Project
<a href="/en/project/GA201%2F01%2F1192" target="_blank" >GA201/01/1192: Research of neural networks capability to provide nonlinear Boolean factor analysis</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2003
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
IASTED International Conference Artificial Intelligence and Applications.
ISBN
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ISSN
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e-ISSN
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Number of pages
8
Pages from-to
300-307
Publisher name
IASTED
Place of publication
Benalmadena, Malaga,
Event location
Benalmadena, Malaga,
Event date
Jan 1, 2003
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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