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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/28018

Title: Prototype Optimization Based on Minimizing Classification Oriented Error Function
Authors: Yea‐Shuan Huang;Cheng‐Chin Chiang;Jun‐Wei Shieh;Eric Grimson
Contributors: NTOU:Department of Computer Science and Engineering
Keywords: neural networks;nearest neighbor classification;prototype optimization
Date: 2001
Issue Date: 2011-10-21T02:35:11Z
Publisher: Journal of the Chinese Institute of Engineers
Abstract: Abstract:A novel method of constructing optimized prototypes for nearest- neighbor classification is proposed. Based on an effective classification oriented error function containing class identification and class separation components, the corresponding updating rules for prototypes and feature weights are derived. By minimizing the error function, the optimized prototypes and feature weights from the nearest-neighbor classification point of view can be effectively constructed. The proposed method consists of several distinct process; Second, multiple prototypes not belonging to the true class of input sample x are updated when x is classified incorrectly; Third, it intrinsically assigns different learning factors to training samples, which enables a large amount of learning from constructive samples, and limited learning from outlier ones; Fourth, by adding a class separation component it avoids the degenerated situation where different prototypes coincide at the same feature position. Experiment results have shown that the proposed methods superior to LVQ2 and other method in previous work (Huang, 1995).
Relation: 24(6), pp.771-780
URI: http://ntour.ntou.edu.tw/handle/987654321/28018
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