Prototype Learning Algorithms for Nearest Neighbor Classifier with Application to Handwritten Character Recognition

Authors:
Cheng-Lin Liu;Masaki Nakagawa
Affiliations:
-;-
Venue:
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
Year:
1999

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Abstract

This paper reviews some prototype learning algorithms for nearest neighbor (NN) classifier design and 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 Sato98 and a new algorithm MAXP2 yield best results.