'Say EM' for Selecting Probabilistic Models for Logical Sequences
  Many real world sequences such as protein secondary structures or shell logs
exhibit a rich internal structures. Traditional probabilistic models of
sequences, however, consider sequences of flat symbols only. Logical hidden
Markov models have been proposed as one solution. They deal with logical
sequences, i.e., sequences over an alphabet of logical atoms. This comes at the
expense of a more complex model selection problem. Indeed, different
abstraction levels have to be explored. In this paper, we propose a novel
method for selecting logical hidden Markov models from data called SAGEM. SAGEM
combines generalized expectation maximization, which optimizes parameters, with
structure search for model selection using inductive logic programming
refinement operators. We provide convergence and experimental results that show
SAGEM's effectiveness.
