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

Generalisation of a language sample for grammatical inference of GDPLL(k) grammars

In the paper we present the method of the generalisation of a language sample for grammatical inference of quasi context-sensitive GDPLL(k) grammars. GDPLL(k) grammars and parsers have been developed as an efficient tool for syntactic pattern recognition: the grammars are characterised by very good discriminative properties and the parser for the grammars is of the linear computational complexity. Nevertheless, one of the main problems of practical application of GDPLL(k) grammars in syntactic pattern recognition systems consists in difficulties in defining the grammar from the sample of a pattern language. The method which we describe in the paper is an important element of the solution of this problem