Binary Factorization in Hopfield-Like Neural Autoassociator: A Promising Tool for Data Compression.

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F03%3A06030184" target="_blank" >RIV/67985807:_____/03:06030184 - isvavai.cz</a>

Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Binary Factorization in Hopfield-Like Neural Autoassociator: A Promising Tool for Data Compression.

Original language description

Proposed approach of data compression is based on feature extraction procedure which maps original patterns into features (factors) space of reduced, possibily very small, dimension. It is shown that Hebbian unsupervised learning of Hopfield-like neuralnetwork is a natural procedure for factor extraction. Due to this learning, factors become the attractors of network dynamics, hence they can be revealed by the random search.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

JD - Use of computers, robotics and its application

OECD FORD branch

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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)<br>Z - Vyzkumny zamer (s odkazem do CEZ)

Publication year

2003

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Artificial Neural Nets and Genetic Algorithms.

ISBN

3-211-00743-1

ISSN

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e-ISSN

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Number of pages

5

Pages from-to

58-62

Publisher name

Springer-Verlag

Place of publication

Wien

Event location

Roanne [FR]

Event date

Apr 23, 2003

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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