Learning Bayesian network is a problem to obtain a network that is the most appropriate to training dataset based on the evaluation measures given. It is studied to decrease time and effort for designing Bayesian networks. In this paper, we propose a novel online learning method of Bayesian network parameters. It provides high flexibility through learning from incomplete data and provides high adaptability on environments through online learning. We have confirmed the performance of the proposed method through the comparison with Voting EM algorithm, which is an online parameter learning method proposed by Cohen, et al. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Lim, S., & Cho, S. B. (2006). Online learning of Bayesian network parameters with incomplete data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4114 LNAI-II, pp. 309–314). Springer Verlag. https://doi.org/10.1007/978-3-540-37275-2_40
Mendeley helps you to discover research relevant for your work.