New BFA Method Based on Attractor Neural Network and Likelihood Maximization
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F14%3A86092386" target="_blank" >RIV/61989100:27240/14:86092386 - isvavai.cz</a>
Alternative codes found
RIV/67985807:_____/14:00398493 RIV/61989100:27740/14:86092386
Result on the web
<a href="http://www.sciencedirect.com/science/article/pii/S0925231213010758" target="_blank" >http://www.sciencedirect.com/science/article/pii/S0925231213010758</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.neucom.2013.07.047" target="_blank" >10.1016/j.neucom.2013.07.047</a>
Alternative languages
Result language
angličtina
Original language name
New BFA Method Based on Attractor Neural Network and Likelihood Maximization
Original language description
What is suggested is a new approach to Boolean factor analysis, which is an extension of the previously proposed Boolean factor analysis method: Hopfield-like attractor neural network with increasing activity. We increased its applicability and robustness when complementing this method by a maximization of the learning set likelihood function defied according to the Noisy-OR generative model. We demonstrated the efficiency of the new method using the data set generated according to the model. Successfulapplication of the method to the real data is shown when analyzing the data from the Kyoto Encyclopedia of Genes and Genomes database which contains full genome sequencing for 1368 organisms.
Czech name
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Czech description
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Classification
Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Neurocomputing
ISSN
0925-2312
e-ISSN
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Volume of the periodical
132,
Issue of the periodical within the volume
MAY 20 2014
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
16
Pages from-to
14-29
UT code for WoS article
000334480500003
EID of the result in the Scopus database
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