TY - CHAP
T1 - Using Feature Hierarchies in Bayesian Network Learning
T2 - Abstraction, Reformulation, and Approximation
Y1 - 2000
A1 - desJardins, Marie
A1 - Getoor,Lise
A1 - Koller,Daphne
ED - Choueiry,Berthe
ED - Walsh,Toby
AB - In recent years, researchers in statistics and the UAI community have developed an impressive body of theory and algorithmic machinery for learning Bayesian networks from data. Learned Bayesian networks can be used for pattern discovery, prediction, diagnosis, and density estimation tasks. Early pioneering work in this area includes [ 5 , 9 , 10 , 13 ]. The algorithm that has emerged as the current most popular approach is a simple greedy hill-climbing algorithm that searches the space of candidate structures, guided by a network scoring function (either Bayesian or Minimum Description Length (MDL)-based). The search begins with an initial candidate network (typically the empty network, which has no edges), and then considers making small local changes such as adding, deleting, or reversing an edge in the network.
JA - Abstraction, Reformulation, and Approximation
T3 - Lecture Notes in Computer Science
PB - Springer Berlin / Heidelberg
VL - 1864
SN - 978-3-540-67839-7
UR - http://dx.doi.org/10.1007/3-540-44914-0_16
ER -