Prototype learning algorithms for nearest neighbor classifier with application to handwritten character recognition

This paper reviews some prototype learning algorithms for nearest neighbor (NN) classifier design land evaluates their performances in handwritten character recognition. The algorithms include the well-known LVQ and those that globally optimize an objective function, as well as some newly derived variants. Experimental results of handwritten numeral recognition and Chinese character recognition show that the global optimization algorithms generally outperform LVQ. Particularly, the generalized LVQ of Sato and Yamada (1998) and a new algorithm MAXP2 yield best results