7
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: not found

      Structure learning of probabilistic logic programs by searching the clause space

      ,
      Theory and Practice of Logic Programming
      Cambridge University Press (CUP)

      Read this article at

      ScienceOpenPublisher
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Learning probabilistic logic programming languages is receiving an increasing attention, and systems are available for learning the parameters (PRISM, LeProbLog, LFI-ProbLog and EMBLEM) or both structure and parameters (SEM-CP-logic and SLIPCASE) of these languages. In this paper we present the algorithm SLIPCOVER for “Structure LearnIng of Probabilistic logic programs by searChing OVER the clause space.” It performs a beam search in the space of probabilistic clauses and a greedy search in the space of theories using the log likelihood of the data as the guiding heuristics. To estimate the log likelihood, SLIPCOVER performs Expectation Maximization with EMBLEM. The algorithm has been tested on five real world datasets and compared with SLIPCASE, SEM-CP-logic, Aleph and two algorithms for learning Markov Logic Networks (Learning using Structural Motifs (LSM) and ALEPH++ExactL1). SLIPCOVER achieves higher areas under the precision-recall and receiver operating characteristic curves in most cases.

          Related collections

          Most cited references30

          • Record: found
          • Abstract: not found
          • Article: not found

          The well-founded semantics for general logic programs

            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Learning multiple evolutionary pathways from cross-sectional data.

            We introduce a mixture model of trees to describe evolutionary processes that are characterized by the ordered accumulation of permanent genetic changes. The basic building block of the model is a directed weighted tree that generates a probability distribution on the set of all patterns of genetic events. We present an EM-like algorithm for learning a mixture model of K trees and show how to determine K with a maximum likelihood approach. As a case study, we consider the accumulation of mutations in the HIV-1 reverse transcriptase that are associated with drug resistance. The fitted model is statistically validated as a density estimator, and the stability of the model topology is analyzed. We obtain a generative probabilistic model for the development of drug resistance in HIV that agrees with biological knowledge. Further applications and extensions of the model are discussed.
              Bookmark
              • Record: found
              • Abstract: not found
              • Book: not found

              Introduction to Statistical Relational Learning

                Bookmark

                Author and article information

                Journal
                Theory and Practice of Logic Programming
                Theory and Practice of Logic Programming
                Cambridge University Press (CUP)
                1471-0684
                1475-3081
                March 2015
                January 15 2014
                March 2015
                : 15
                : 2
                : 169-212
                Article
                10.1017/S1471068413000689
                086537b3-1b06-4de0-8622-67f2cc6fc00b
                © 2015

                https://www.cambridge.org/core/terms

                History

                Comments

                Comment on this article