%0 Journal Article
%T Learning the Structure of Bayesian Network from Small Amount of Data
%A Adina COCU
%A Marian Viorel CRACIUN
%A Bogdan COCU
%J Annals of Dunarea de Jos
%D 2009
%I Universitatea Dunarea de Jos
%X Many areas of artificial intelligence must handling with imperfection ofinformation. One of the ways to do this is using representation and reasoning withBayesian networks. Creation of a Bayesian network consists in two stages. First stage isto design the node structure and directed links between them. Choosing of a structurefor network can be done either through empirical developing by human experts orthrough machine learning algorithm. The second stage is completion of probabilitytables for each node. Using a machine learning method is useful, especially when wehave a big amount of leaning data. But in many fields the amount of data is small,incomplete and inconsistent. In this paper, we make a case study for choosing the bestlearning method for small amount of learning data. Means more experiments we dropconclusion of using existent methods for learning a network structure.
%K Bayesian network
%K machine learning algorithm
%K structure learning
%U http://www.ann.ugal.ro/eeai/archives/2009/Lucrare-2-ACocu-p12-16.pdf