A NEW EDLA-BASED FRAMEWORK FOR FINDING OPTIMAL STOCHASTIC SUB-GRAPH

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MOLLAKHALILI MEYBODI M.R.; MEYBODI M.R.

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Paper Information

Journal: NASHRIYYAH -I MUHANDISI -I BARQ VA MUHANDISI -I KAMPYUTAR -I IRAN, B- MUHANDISI -I KAMPYUTAR Year:2015 | Volume:12 | Issue:2 Page(s): 85-97

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Information Journal Paper

Title

A NEW EDLA-BASED FRAMEWORK FOR FINDING OPTIMAL STOCHASTIC SUB-GRAPH

Author(s)

MEYBODI M.R. target="_blank">MOLLAKHALILI MEYBODI M.R. | MEYBODI M.R. | Issue Writer Certificate

Keywords

LEARNING AUTOMATAQ2

DISTRIBUTED LEARNING AUTOMATAQ2

EXTENDED DISTRIBUTED LEARNING AUTOMATA (EDLA)Q2

STOCHASTIC GRAPHQ2

STOCHASTIC SUB-GRAPHQ2

Abstract

In this paper a new structure of LEARNING AUTOMATA which is called as extended distributed LEARNING AUTOMATA (eDLA) is introduced. A new eDLA-based iterative sampling method for finding optimal sub-graph in STOCHASTIC GRAPHs is proposed. Some mathematical analysis of the proposed algorithm is presented and the convergence property of the algorithm is studied. Our study shows that the proposed algorithm can be converge to the optimal sub-graph.

Cites

LEANING THE STRUCTURE OF BAYESIAN NETWORKS USING LEARNING AUTOMATA

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APA: Copy

MOLLAKHALILI MEYBODI, M.R., & MEYBODI, M.R.. (2015). A NEW EDLA-BASED FRAMEWORK FOR FINDING OPTIMAL STOCHASTIC SUB-GRAPH. NASHRIYYAH -I MUHANDISI -I BARQ VA MUHANDISI -I KAMPYUTAR -I IRAN, B- MUHANDISI -I KAMPYUTAR, 12(2), 85-97. SID. https://sid.ir/paper/228391/en

Vancouver: Copy

MOLLAKHALILI MEYBODI M.R., MEYBODI M.R.. A NEW EDLA-BASED FRAMEWORK FOR FINDING OPTIMAL STOCHASTIC SUB-GRAPH. NASHRIYYAH -I MUHANDISI -I BARQ VA MUHANDISI -I KAMPYUTAR -I IRAN, B- MUHANDISI -I KAMPYUTAR[Internet]. 2015;12(2):85-97. Available from: https://sid.ir/paper/228391/en

IEEE: Copy

M.R. MOLLAKHALILI MEYBODI, and M.R. MEYBODI, “A NEW EDLA-BASED FRAMEWORK FOR FINDING OPTIMAL STOCHASTIC SUB-GRAPH,” NASHRIYYAH -I MUHANDISI -I BARQ VA MUHANDISI -I KAMPYUTAR -I IRAN, B- MUHANDISI -I KAMPYUTAR, vol. 12, no. 2, pp. 85–97, 2015, [Online]. Available: https://sid.ir/paper/228391/en

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