On the estimation of error-correcting parameters
Juan-Carlos Amengual, Enrique Vidal · 2002
Error-correcting (EC) techniques allow for coping with divergences in pattern strings with regard to their "standard" form as represented by the language /spl Lscr/ accepted by a regular or context-free grammar. There are two main types of EC parsers: minimum-distance and stochastic. The latter apply the maximum likelihood rule: classification into the classes of the strings in /spl Lscr/ that have the greatest probability given the strings representing unknown patterns. Stochastic models are important in pattern recognition if good estimations for their parameters are provided. The problem of parameter estimation has been well studied for stochastic grammars, but this is not the case of EC parameters. This work is aimed at providing solutions to adequately solve it.