Abstract
The k-Nearest Neighbour rule is one of the most popular non-parametric classification techniques in Pattern Recognition. This technique requires a set of good prototypes to represent pattern classes. One possibility is to define the given training set as the set of prototypes. Obviously, this approach presents a high computational cost if the training set is large. Alternatively, clustering techniques allow for the description of a training corpus in terms of clusters. A cluster is formed by patterns with certain simmilarities [1]. These clusters can be represented by a set of prototypes. The selection of adequate prototypes is one of the most important problems in Pattern Recognition.KeywordsEdit DistanceMarkov NetworkLevenshtein DistanceGood Classification ResultPattern Recognition LetterThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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