Inferring probabilistic acyclic automata using the minimum description length principle

Abstract

The use of Rissanen's (1978) minimum description length principle for the construction of probabilistic acyclic automata (PAA) is explored. We propose a learning algorithm for a PAA that is adaptive both in the structure and the dimension of the model. The proposed algorithm was tested on synthetic data as well as on real pattern recognition problems.<>