Now showing items 1-5 of 6

  • Clustering Relational Data Based on Randomized Propositionalization

    Anderson, Grant; Pfahringer, Bernhard (Springer, Berlin, 2008)
    Clustering of relational data has so far received a lot less attention than classification of such data. In this paper we investigate a simple approach based on randomized propositionalization, which allows for applying ...
  • Exploiting propositionalization based on random relational rules for semi-supervised learning

    Pfahringer, Bernhard; Anderson, Grant (Springer, Berlin, 2008)
    In this paper we investigate an approach to semi-supervised learning based on randomized propositionalization, which allows for applying standard propositional classification algorithms like support vector machines to ...
  • Idioms for µ-charts

    Anderson, Grant; Reeve, Greg; Reeves, Steve (IEEE COMPUTER SOC, 2001-08-01)
    This paper presents an idiomatic construct for µ-charts which reflects the high-level specification construct of synchronization between activities. This, amongst others, has emerged as a common and useful idea during our ...
  • Random Relational Rules

    Pfahringer, Bernhard; Anderson, Grant (2006)
    Exhaustive search in relational learning is generally infeasible, therefore some form of heuristic search is usually employed, such as in FOIL[1]. On the other hand, so-called stochastic discrimination provides a framework ...
  • Random Relational Rules

    Anderson, Grant (The University of Waikato, 2008)
    In the field of machine learning, methods for learning from single-table data have received much more attention than those for learning from multi-table, or relational data, which are generally more computationally complex. ...

Grant Anderson has 3 co-authors in Research Commons.