bibtype	C - Conference Paper (international conference)
ARLID	0085999
utime	20240103184438.7
mtime	20070924235959.9
title (primary) (eng)	Using imsets for learning Bayesian networks
specification	page_count 12 s.
serial	ARLID cav_un_epca*0085998 title Proceedings of Czech-Japan Seminar on Data Analysis and Decision Making under Uncertainty /10./ page_num 178-189 publisher place Praha name UTIA AV ČR year 2007 editor name1 Kroupa name2 T. editor name1 Vejnarová name2 J.
title (cze)	Využití imsetů při učení bayesovských sítí
keyword	Bayesian networks
keyword	artificial intelligence
keyword	probabilistic graphical models
keyword	machine learning
author (primary)	ARLID cav_un_auth*0101228 name1 Vomlel name2 Jiří institution UTIA-B full_dept Department of Decision Making Theory fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author	ARLID cav_un_auth*0101202 name1 Studený name2 Milan institution UTIA-B full_dept Department of Decision Making Theory fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
cas_special	project project_id 1M0572 agency GA MŠk country CZ ARLID cav_un_auth*0001814 project project_id 2C06019 agency GA MŠk country CZ ARLID cav_un_auth*0216518 research CEZ:AV0Z10750506 abstract (eng) 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. abstract (cze) Článek popisuje implementaci hladového algoritmu pro učení baysovských sítí. Algoritmus je založen na algebraických objektech - tzv. imsetech a na prohledávání tzv. inkluzivního okolí. action ARLID cav_un_auth*0230109 name Czech-Japan Seminar on Data Analysis and Decision Making under Uncertainty /10./ place Liblice dates 15.09.2007-18.09.2007 country CZ reportyear 2008 RIV BB permalink http://hdl.handle.net/11104/0148380 arlyear 2007 mrcbU63 cav_un_epca*0085998 Proceedings of Czech-Japan Seminar on Data Analysis and Decision Making under Uncertainty /10./ 178 189 Praha UTIA AV ČR 2007 mrcbU67 Kroupa T. 340 mrcbU67 Vejnarová J. 340