PMC:1459173 / 21100-22181 JSONTXT

{"target":"https://pubannotation.org/docs/sourcedb/PMC/sourceid/1459173","sourcedb":"PMC","sourceid":"1459173","source_url":"https://www.ncbi.nlm.nih.gov/pmc/1459173","text":"The methods presented here belong to a class of off-line textual substitution that try to reap through greedy approximation the benefits of otherwise intractable optimal macro schemes [9]. The specific heuristic followed here is based on a greedy iterative selection (see e.g., [10]) which consists of identifying and using, at each iteration, a substring w of the text x such that encoding all instances of w in x yields the highest possible contraction of x. This process may be also interpreted as learning a \"straight line\" grammar of minimum description length for the sourcestring, for which we refer to [5,11,12] and references therein. Off-line methods are not always practical and can be computationally imposing even in approximate variants. They do find use in contexts and applications, such as mass production of CD-ROMs, backup archiving, etc. (see, e.g., [13]). Paradigms of steepest descent approximations have delivered good performances in practice and also appear to be the best candidates in terms of the approximation achieved to optimum descriptor sizes [14].","tracks":[]}