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Relaxation-based structure learning of dynamic bayesian Networks / Farida Mohamed Sabry Elsayed ; Supervised Nevin M. Darwish , Magda B. Fayek

By: Contributor(s): Material type: TextTextLanguage: English Publication details: Cairo : Farida Mohamed Sabry Elsayed , 2009Description: 114 P. : plans ; 25cmOther title:
  • تعلم هياكل شبكات بيزيان الديناميكية باسلوب الارخاء [Added title page title]
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Dissertation note: Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Computer Engineering Summary: Learning DBN structures from data is a relatively new research direction. This work uses probabilistic relaxation for learning dynamic bayesian network structures from data. The existence of an edge in the network is not considered as a hard or deterministic issue, but rather we assign a certain probability for the existence of each edge
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Item type Current library Home library Call number Copy number Status Date due Barcode
Thesis Thesis قاعة الرسائل الجامعية - الدور الاول المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.13.06.M.Sc.2009.Fa.R (Browse shelf(Opens below)) Not for loan 01010110051382000
CD - Rom CD - Rom مخـــزن الرســائل الجـــامعية - البدروم المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.13.06.M.Sc.2009.Fa.R (Browse shelf(Opens below)) 51382.CD Not for loan 01020110051382000

Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Computer Engineering

Learning DBN structures from data is a relatively new research direction. This work uses probabilistic relaxation for learning dynamic bayesian network structures from data. The existence of an edge in the network is not considered as a hard or deterministic issue, but rather we assign a certain probability for the existence of each edge

Issued also as CD

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