A prototype classification method and its use in a hybrid solution for multiclass pattern recognition |
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Authors: | Chien-Hsing Chou [Author Vitae] |
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Affiliation: | a Institute of Information Science, Academia Sinica, 128 Section 2, Academia Road, Nankang, Taipei 115, Taiwan b Department of Electrical Engineering, National Taipei University of Technology, Taipei, Taiwan |
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Abstract: | 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 method 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. |
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Keywords: | Fuzzy c-means clustering algorithm Handwritten character recognition Hybrid classifier K-means clustering algorithm Prototype learning Support vector machine |
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