TY - GEN
T1 - Closed-form solutions in learning probabilistic logic programs by exact score maximization
AU - Otte Vieira de Faria, Francisco Henrique
AU - Cozman, Fabio Gagliardi
AU - Mauá, Denis Deratani
PY - 2017/1/1
Y1 - 2017/1/1
N2 - © Springer International Publishing AG 2017. We present an algorithm that learns acyclic propositional probabilistic logic programs from complete data, by adapting techniques from Bayesian network learning. Specifically, we focus on score-based learning and on exact maximum likelihood computations. Our main contribution is to show that by restricting any rule body to contain at most two literals, most needed optimization steps can be solved exactly. We describe experiments indicating that our techniques do produce accurate models from data with reduced numbers of parameters.
AB - © Springer International Publishing AG 2017. We present an algorithm that learns acyclic propositional probabilistic logic programs from complete data, by adapting techniques from Bayesian network learning. Specifically, we focus on score-based learning and on exact maximum likelihood computations. Our main contribution is to show that by restricting any rule body to contain at most two literals, most needed optimization steps can be solved exactly. We describe experiments indicating that our techniques do produce accurate models from data with reduced numbers of parameters.
UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85030837069&origin=inward
UR - https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=85030837069&origin=inward
U2 - 10.1007/978-3-319-67582-4_9
DO - 10.1007/978-3-319-67582-4_9
M3 - Conference contribution
SN - 9783319675817
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 119
EP - 133
BT - Closed-form solutions in learning probabilistic logic programs by exact score maximization
T2 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Y2 - 1 January 2018
ER -