A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is
Lecture Notes in Computer Science
It is well known that the convergence of a grammatical inference method is strongly conditioned by the training data set. Structural completeness is a desired property seldom achieved in real data. The question that naturally arises in these types of problems is: how far is the training data to achieve structural completeness and what is the minimal sample size to use when there is no a priori knowledge about the structure of the data. In this paper we propose a simple methodology to give somedoi:10.1007/bfb0033320 fatcat:uc6np72shngnjnttxiwfhr5knq