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Method and apparatus for designing a highly reliable pattern recognition system

机译:设计高度可靠的模式识别系统的方法和设备

摘要

A design for a high reliability recognition system utilizes two optimized thresholds for each class k of a prototype data base. One threshold is a class region threshold CR.sub.k and the other is a dis- ambiguity threshold DA.sub.k. CR.sub.k specifies a constrained region belonging to a class k, and DA.sub.k corresponds to a value with which a sample belonging to class k can be correctly recognized with a high level of confidence. During recognition, if the distance D(x, r.sub.M) between an input sample x and the representative prototype r.sub.M of a nearest class M is larger than the class region threshold CR.sub.M, x will be rejected. Furthermore, if the distance D(x, r.sub.M) is subtracted from the distance D(x, r.sub.S) between x and the representative prototype r. sub.S of a second nearest class S, the resulting distance difference must be greater than the dis-ambiguity threshold DA.sub.M, or x will be rejected. An inventive algorithm is used to compute optimum thresholds CR. sub.k and DA.sub.k for each class k. The algorithm is based on minimizing a cost function of a recognition error analysis. Experiments were performed to verify the feasibility and effectiveness of the inventive method.
机译:高可靠性识别系统的设计针对原型数据库的每个类别k使用两个优化阈值。一个阈值是类别区域阈值CRk,另一个阈值是歧义阈值DAk。 CR k指定属于类别k的约束区域,而DA k对应于可以高置信度正确识别属于类别k的样本的值。在识别期间,如果输入样本x与最接近的M类的代表性原型rM之间的距离D(x,rM)大于类区域阈值CRM,则x将被拒绝。此外,如果从x与代表性原型r之间的距离D(x,rS)减去距离D(x,rM)。在第二个最接近的类别S中,如果所得的距离差必须大于模糊度阈值DAM,否则x将被拒绝。本发明的算法用于计算最佳阈值CR。每个类别k的sub.k和DA.sub.k。该算法基于最小化识别误差分析的成本函数。进行实验以验证本发明方法的可行性和有效性。

著录项

  • 公开/公告号US5940535A

    专利类型

  • 公开/公告日1999-08-17

    原文格式PDF

  • 申请/专利权人 INDUSTRIAL TECHNOLOGY RESEARCH INSTITUTE;

    申请/专利号US19960741740

  • 发明设计人 YEA-SHUAN HUANG;

    申请日1996-10-31

  • 分类号G06K9/46;

  • 国家 US

  • 入库时间 2022-08-22 02:07:28

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