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    多类模式识别的动态多叉树算法研究与实现

    Research and Realization of a Dynamic Multi Branches Tree Algorithm for Multi Classes Pattern Recognition

    • 摘要: 研究模式识别方法 提出动态多叉树算法 ,用以解决实际环境中复杂的或大模式类别学习及系统动态扩展问题 ,该算法利用分治和局部最优原理缩小目标范围 ,结合整体学习方法提高识别率 ,模拟人脑的循序渐进学习方式 ,实现知识增殖和继承 可解决现有识别系统在学习新知识会破坏已有知识 ,需重新学习的问题 并具有较高的识别率 ,可有效地处理巨模式类识别的问题 该系统可以用于人脸、字符、指纹等对象的识别分类 系统的构造方法体现其通用性 ,性能分析表明其可行性 ,实验结果证明其有效性

       

      Abstract: Pattern recognition methods of a large number of pattern classes are studied A dynamic multi branches algorithm is presented, which can implement knowledge-increasable to solve the problems of complex or large quantities of classes learning and dynamic extension of system It can reduce object scope by using the divide-and-conquer principle; it can improve recognition rate by using combination classifiers; it can learn as human brain does The algorithm can not only accelerate calculation speed and improve recognition rate but also be extended easily and freely It can be used in pattern recognition such as faces, fingerprints, and characters The constructive method of the system shows the generality The capability analysis validates the practicability of the algorithm and the experimentation result proves its reasonableness and feasibility

       

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