TY - CONF

T1 - Speeding up the learning of equivalence classes of Bayesian network structures

AU - Daly, Ronan

AU - Aitken, Stuart

AU - Shen, Qiang

N1 - R. Daly, Q. Shen and S. Aitken. Speeding up the learning of equivalence classes of Bayesian network structures. Proceedings of the 10th International Conference on Artificial Intelligence and Soft Computing, pages 34-39.

PY - 2006

Y1 - 2006

N2 - For some time, learning Bayesian networks has been both
feasible and useful in many problems domains. Recently
research has been done on learning equivalence classes of
Bayesian networks, i.e. structures that capture all of the
graphical information of a group of Bayesian networks,
in order to increase learning speed and quality. However
learning speed still remains quite slow, especially on
problems with many variables. This work aims to describe
a method to speed up algorithm learning speed. A brief
overview of learning Bayesian networks is given. A
method is then given, so that tests of whether a particular
move is valid can be cached. Finally, experiments are
conducted, which show that applying this caching method
produces a marked increase in learning speed.

AB - For some time, learning Bayesian networks has been both
feasible and useful in many problems domains. Recently
research has been done on learning equivalence classes of
Bayesian networks, i.e. structures that capture all of the
graphical information of a group of Bayesian networks,
in order to increase learning speed and quality. However
learning speed still remains quite slow, especially on
problems with many variables. This work aims to describe
a method to speed up algorithm learning speed. A brief
overview of learning Bayesian networks is given. A
method is then given, so that tests of whether a particular
move is valid can be cached. Finally, experiments are
conducted, which show that applying this caching method
produces a marked increase in learning speed.

M3 - Paper

SP - 34

EP - 39

ER -