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.
This work was partially supported by the Spanish MCT under projects TIC2000-1703-C03-01 and TIC2000-1599-CO2-01.
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Martínez-Hinarejos, C.D., Juan, A., Casacuberta, F. (2003). Prototype Extraction for k-NN Classifiers using Median Strings. In: Chen, D., Cheng, X. (eds) Pattern Recognition and String Matching. Combinatorial Optimization, vol 13. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-0231-5_18
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DOI: https://doi.org/10.1007/978-1-4613-0231-5_18
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