TY - GEN
T1 - Novel algorithm for Bayesian network parameter learning with informative prior constraints
AU - Chang, Rui
AU - Wang, Wei
PY - 2010
Y1 - 2010
N2 - The generalization performance of a learned Bayesian network largely depends on the quality of the prior provided to the learning machine. Indeed, the prior distribution is designed to provide additive domain expert knowledge to the parameters in a Bayesian network which tolerate some variance around these initial counts. The learning task is combinatorial regulates on this initial counts by the data statistics. The use of a prior distribution becomes even more critical in case of scarce data.
AB - The generalization performance of a learned Bayesian network largely depends on the quality of the prior provided to the learning machine. Indeed, the prior distribution is designed to provide additive domain expert knowledge to the parameters in a Bayesian network which tolerate some variance around these initial counts. The learning task is combinatorial regulates on this initial counts by the data statistics. The use of a prior distribution becomes even more critical in case of scarce data.
UR - https://www.scopus.com/pages/publications/79959470918
UR - https://www.scopus.com/pages/publications/79959470918#tab=citedBy
U2 - 10.1109/IJCNN.2010.5596889
DO - 10.1109/IJCNN.2010.5596889
M3 - Conference contribution
AN - SCOPUS:79959470918
SN - 9781424469178
T3 - Proceedings of the International Joint Conference on Neural Networks
BT - 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010
Y2 - 18 July 2010 through 23 July 2010
ER -