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Rejecting Nearest Neighbor Classifier Based on Structural Risk Minimization Principle Self-organization Multiple Region Covering Model |
Hu Zheng-ping; Jia Qian-wen |
School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China |
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Abstract According to the mode of rejecting pattern recognition principles, which is based on “matter description” and “matter separation” in uniform statistical pattern recognition, a rejecting nearest neighbor classifier based on structural risk minimization self-organization multiple region covering model is presented in this paper. This new model is better closer to the actual instance, rather than traditional statistical pattern recognition only using “optimal separation”as its main principle. Firstly, the optimal samples are selected from the training samples based on structural risk minimization, which is used for same class pattern matter description. Then the kNN distinguish is as a following step to identified the exact class. The simulation experimental results show that this method is valid and efficient.
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Received: 27 August 2007
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