Transducer learning in pattern recognition

`Interpretation´ is a general and interesting pattern recognition framework in which a system is considered to input object representations, and output the corresponding interpretations in terms of `semantic messages´ specifying the actions to be carried out as system´s responses. From the syntactic pattern recognition viewpoint, interpretation reduces to formal transduction. The authors propose an efficient and effective algorithm to automatically infer a finite state transducer from a training set of input-output examples of the interpretation problem considered. The proposed algorithm has been shown to identify an important class of transductions known as `subsequential transductions.´ Experimental results are presented showing the performance and capabilities of the proposed method