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On the importance of hidden bias and hidden entropy in representational efficiency of the Gaussian-Bipolar restricted boltzmann machines

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dc.contributor.advisorErzin, Engin
dc.contributor.advisorid0000-0002-2715-2368
dc.contributor.authorIsabekov, Altynbek
dc.contributor.instituteKoç University Graduate School of Sciences and Engineering
dc.contributor.programElectrical and Electronics Engineering
dc.contributor.yokid34503
dc.date.accessioned2024-11-09T22:25:45Z
dc.date.issued2018
dc.description65 leaves : illustrations ; 30 cm.
dc.identifier.urihttps://hdl.handle.net/20.500.14288/5375
dc.keywordsArtificial intelligence
dc.keywordsNeural networks (Computer science)
dc.keywordsYapay öğrenme
dc.keywordsGauss-Bernoulli kısıtlı boltzmann makineleri
dc.languageEnglish
dc.publisherKoç University
dc.relation.collectionKU Theses and Dissertations
dc.rightsrestrictedAccess
dc.rights.copyrightsnote© All Rights Reserved. Accessible to Koç University Affiliated Users Only!
dc.subjectMachine learning
dc.subjectComputational learning theory
dc.thesis.degreeDoctoral Degree
dc.thesis.grantorİstanbul
dc.titleOn the importance of hidden bias and hidden entropy in representational efficiency of the Gaussian-Bipolar restricted boltzmann machines
dc.typeDissertation
dspace.entity.typePublication
relation.isAdvisorOfThesis87fd97a5-902d-42ed-86b0-4624922eec48
relation.isAdvisorOfThesis.latestForDiscovery87fd97a5-902d-42ed-86b0-4624922eec48

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