On the learning of vague languages for syntactic pattern recognition

The method of the learning of vague languages which represent distorted/ambiguous patterns is proposed in the paper. The goal of the method is to infer the quasi-context-sensitive string grammar which is used in our model as the generator of patterns. The method is an important component of the multi-derivational model of the parsing of vague languages used for syntactic pattern recognition

On the analysis of fuzzy string patterns with the help of extended and stochastic GDPLL(k) grammars

Two methods of the analysis of distorted (fuzzy) string patterns are presented. The methods are based on the use of GDPLL(k) grammars generating a large subclass of context sensitive languages. The first one utilizes error-correcting approach: a minimum distance measure is used for error-correcting parsing. The second one utilizes stochastic approach: the decision about the production to be applied in a derivation step is given according to the probability measure