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A prototype classification method and its use in a hybrid solution for multiclass pattern recognition
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DOI：10.1016/j.patcog.2005.10.022.png)
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Abstract
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In this paper, we propose a prototype classification method that employs a learning process to determine both the number and the location of prototypes. This learning process decides whether to stop adding prototypes according to a certain termination condition, and also adjusts the location of prototypes using either the K-means (KM) or the fuzzy c-means (FCM) clustering algorithms. When the prototype classification rnethod is applied, the support vector machine (SVM) method can be used to post-process the top-rank candidates obtained during the prototype learning or matching process. We apply this hybrid solution to handwriting recognition and address the convergence behavior and runtime consumption of the prototype construction process, and discuss how to combine our prototype classifier with SVM classifiers to form an effective hybrid classifier. (c) 2005 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
Keywords:
fuzzy c-means clustering algorithm
handwritten character recognition
hybrid classifier
K-means clustering algorithm
prototype learning
support vector machine
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