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 。
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
出处
《计算机研究与发展》
EI
CSCD
北大核心
2003年第1期115-122,共8页
Journal of Computer Research and Development
基金
国家自然科学基金资助 (69973 0 0 2 )
关键词
多类模式识别
动态多叉树算法
知识增殖
知识继承
相似度矩阵
knowledge-increasable
knowledge-inheritable
pattern recognition
dynamic multi-branches tree algorithm
similarity degree matrix (SDM)
