Bj. Oommen et Evd. Croix, STRING TAXONOMY USING LEARNING AUTOMATA, IEEE transactions on systems, man and cybernetics. Part B. Cybernetics, 27(2), 1997, pp. 354-365
Citations number
46
Categorie Soggetti
Controlo Theory & Cybernetics","Computer Science Cybernetics","Robotics & Automatic Control
A typical syntactic pattern recognition (PR) problem involves comparin
g a noisy string with every element of a dictionary, H. The problem of
classification can be greatly simplified if the dictionary is partiti
oned into a set of subdictionaries. In this case, the classification c
an be hierarchical-the noisy string is first compared to a representat
ive element of each subdictionary and the closest match within the sub
dictionary is subsequently located, Indeed, the entire problem of subd
ividing a set of strings into subsets where each subset contains ''sim
ilar'' strings has been referred to as the ''String Taxonomy Problem.'
' To our knowledge there is no reported solution to this problem (see
footnote 2). In this paper we present a learning-automaton based solut
ion to string taxonomy, The solution utilizes the Object Migrating Aut
omaton (OMA) the power of which in clustering objects and images [33],
[35] has been reported, The power of the scheme for string taxonomy h
as been demonstrated using random strings and garbled versions of stri
ng representations of fragments of macromolecules.