Recently much work in Machine Learning has concentrated on representation languages able to combine aspects of logic and probability, leading to the birth of a whole eld called Statistical Relational Learning. In this paper we present a technique for parameter learning targeted to a family of formalisms where uncertainty is represented using Logic Programming tools - the so-called Probabilistic Logic Programs such as ICL, PRISM, ProbLog and LPAD. Since their equivalent Bayesian networks contain hidden variables an EM algorithm is adopted. In order to speed the computation, expectations are computed directly on the Binary Decision Diagrams that are built for inference. The resulting system, called Emblem for \EM over BDDs for probabilistic Logic programs Ecient Mining", has been applied to a number of datasets and showed good performances both in terms of speed and memory usage.
EM over Binary Decision Diagrams for Probabilistic Logic Programs
BELLODI, Elena;RIGUZZI, Fabrizio
2011
Abstract
Recently much work in Machine Learning has concentrated on representation languages able to combine aspects of logic and probability, leading to the birth of a whole eld called Statistical Relational Learning. In this paper we present a technique for parameter learning targeted to a family of formalisms where uncertainty is represented using Logic Programming tools - the so-called Probabilistic Logic Programs such as ICL, PRISM, ProbLog and LPAD. Since their equivalent Bayesian networks contain hidden variables an EM algorithm is adopted. In order to speed the computation, expectations are computed directly on the Binary Decision Diagrams that are built for inference. The resulting system, called Emblem for \EM over BDDs for probabilistic Logic programs Ecient Mining", has been applied to a number of datasets and showed good performances both in terms of speed and memory usage.I documenti in SFERA sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
