A Markovian approach to the induction of regular string distributions

We propose in this paper a novel approach to the induction of the structure of Hidden Markov Models (HMMs). The notion of partially observable Markov models (POMMs) is introduced. POMMs form a particular case of HMMs where any state emits a single letter with probability one, but several states can emit the same letter. It is shown that any HMM can be represented by an equivalent POMM. The proposed induction algorithm aims at finding a POMM fitting a sample drawn from an unknown target POMM. The induced model is built to fit the dynamics of the target machine observed in the sample. A POMM is seen as a lumped process of a Markov chain and the induced POMM is constructed to best approximate the stationary distribution and the mean first passage times (MFPT) observed in the sample. The induction relies on iterative state splitting from an initial maximum likelihood model. The transition probabilities of the updated model are found by solving an optimization problem to minimize the difference between the observed MFPT and their values computed in the induced model

eg-GRIDS: Context-Free Grammatical Inference from Positive Examples using Genetic Search

Abstract. In this paper we present eg-GRIDS, an algorithm for inducing context-free grammars that is able to learn from positive sample sentences. The presented algorithm, similar to its GRIDS predecessors, uses simplicity as a criterion for directing inference, and a set of operators for exploring the search space. In addition to the basic beam search strategy of GRIDS, eg-GRIDS incorporates an evolutionary grammar selection process, aiming to explore a larger part of the search space. Evaluation results are presented on artificially generated data, comparing the performance of beam search and genetic search. These results show that genetic search performs better than beam search while being significantly more efficient computationally