Solomonoff coding as a means of introducing prior information in syntactic pattern recognition

In this paper, a solution to the problem of introducing a priori information in phrase structured syntactic pattern recognition systems is proposed. The method consists of devising subpatterns (representing regularities in the symbol sequences) by means of Solomonoff coding, and using these subpatterns as a priori information for constraining the production of structural samples, which serve as input to grammar inference algorithms. We have shown, in a previous work, the adequacy of syntactic modeling in automatic sleep analysis. In this paper, a further improvement is achieved by applying the proposed methodology.