Learning and Unlearning in Hopfield-Like Neural Network Performing Boolean Factor Analysis
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F10%3A00331155" target="_blank" >RIV/67985807:_____/10:00331155 - 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
Learning and Unlearning in Hopfield-Like Neural Network Performing Boolean Factor Analysis
Original language description
The peculiarity of usage the Hopfield-like network for Boolean factor analysis is the appearance of two global spurious attractors. They become dominant and, therefore, prevent successful factors search. To eliminate these attractors we propose a specialunlearning procedure. This second unlearning procedure provides the suppression of factors with the largest attraction basins which dominate after suppression of global spurious attractors and prevent the recall of other factors. The origin of the global spurious attractors and the efficiency of the unlearning procedures are investigated in the present paper.
Czech name
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Czech description
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Classification
Type
C - Chapter in a specialist book
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
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Continuities
Z - Vyzkumny zamer (s odkazem do CEZ)
Others
Publication year
2010
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Book/collection name
Advances in Machine Learning I
ISBN
978-3-642-05176-0
Number of pages of the result
18
Pages from-to
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Number of pages of the book
529
Publisher name
Springer
Place of publication
Berlin
UT code for WoS chapter
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