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A Mean Field Approximation of BCPNN
KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. (Lansner)ORCID iD: 0000-0002-2358-7815
KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
2005 (English)Report (Other academic)
Abstract [en]

In this report we study a mean field (MF) approximation of the Bayesian Confidence Propagating Neural Network (BCPNN) for which we derive the energy function. This MF approximation is compared with the original formulation of the network in a number of different tasks in order to establish the similarities and dissimilarities. We investigate the effect of different updating strategies on the storage capacity. Three different ways of modulating the attractor size are experimentally tested. We apply the networks to prototype extraction. Finally, we investigate how the networks cluster data. These experiments show that there are some differences between BCPNN and its MF approximation. Furthermore, the experiments provide some new knowledge on the clustering of memories in a BCPNN.

Place, publisher, year, edition, pages
KTH, 2005. , 19 p.
Series
TRITA-NA, ISSN 0348-2952 ; 0506TRITA-NA-P, 0506
Keyword [en]
Neural network, mean field, BCPNN
National Category
Communication Systems
Identifiers
URN: urn:nbn:se:kth:diva-125022OAI: oai:DiVA.org:kth-125022DiVA: diva2:638892
Funder
Swedish Research Council
Note

QC 20150127

Available from: 2013-08-04 Created: 2013-08-04 Last updated: 2015-01-27Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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  • Other locale
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Output format
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  • text
  • asciidoc
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