Original title: Using imsets for learning Bayesian networks
Translated title: Využití imsetů při učení bayesovských sítí
Authors: Vomlel, Jiří ; Studený, Milan
Document type: Papers
Conference/Event: Czech-Japan Seminar on Data Analysis and Decision Making under Uncertainty /10./, Liblice (CZ), 2007-09-15 / 2007-09-18
Year: 2007
Language: eng
Abstract: [eng] [cze]
This paper describes a modification of the greedy equivalence search (GES) algorithm. The presented modification is based on the algebraic approach to learning. The states of the search space are standard imsets. Each standard imset represents an equivalence class of Bayesian networks. For a given quality criterion the database is represented by the respective data imset. This allows a very simple update of a given quality criterion since the moves between states are represented by differential imsets. We exploit a direct characterization of lower and upper inclusion neighborhood, which allows an efficient search for the best structure in the inclusion neighborhood. The algorithm was implemented in R and is freely available. Článek popisuje implementaci hladového algoritmu pro učení baysovských sítí. Algoritmus je založen na algebraických objektech - tzv. imsetech a na prohledávání tzv. inkluzivního okolí.
Keywords: artificial intelligence; Bayesian networks; machine learning; probabilistic graphical models
Project no.: CEZ:AV0Z10750506 (CEP), 1M0572 (CEP), 2C06019 (CEP)
Funding provider: GA MŠk, GA MŠk
Host item entry: Proceedings of Czech-Japan Seminar on Data Analysis and Decision Making under Uncertainty /10./

Institution: Institute of Information Theory and Automation AS ČR (web)
Document availability information: Fulltext is available at the institute of the Academy of Sciences.
Original record: http://hdl.handle.net/11104/0148380

Permalink: http://www.nusl.cz/ntk/nusl-37400


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Research > Institutes ASCR > Institute of Information Theory and Automation
Conference materials > Papers