| Full text | |
| Author(s): |
Otte Vieira de Faria, Francisco Henrique
;
Cozman, Fabio Gagliardi
;
Maua, Denis Deratani
;
Moral, S
;
Pivert, O
;
Sanchez, D
;
Marin, N
Total Authors: 7
|
| Document type: | Journal article |
| Source: | SCALABLE UNCERTAINTY MANAGEMENT (SUM 2017); v. 10564, p. 15-pg., 2017-01-01. |
| Abstract | |
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. (AU) | |
| FAPESP's process: | 15/21880-4 - PROVERBS -- PRobabilistic OVERconstrained Boolean Systems: reasoning tools and applications |
| Grantee: | Marcelo Finger |
| Support Opportunities: | Regular Research Grants |
| FAPESP's process: | 16/01055-1 - Learning of Tractable Probabilistic Models with Application to Multilabel Classification |
| Grantee: | Denis Deratani Mauá |
| Support Opportunities: | Regular Research Grants |
| FAPESP's process: | 16/18841-0 - Inference and learning algorithms for probabilistic logic programming |
| Grantee: | Fabio Gagliardi Cozman |
| Support Opportunities: | Research Grants - Research Partnership for Technological Innovation - PITE |