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复杂模式
    Novel Methods for High-Dimensional Complex Patterns Recognition
    高维复杂模式识别的新方法
    Topology-preserving map of complex patterns group and its application
    复杂模式保留拓扑的平面映射及其应用
    Knowledge Discovery in Databases (KDD) is an important domain in AI. In this paper, we present a novel logical representation and a new modelling algorithm for the temporal sequential patterns to solve the problem of the complex patterns in temporal data.
    数据库中的知识发现(Knowledge Discovery in Databases,简称KDD)是人工智能领域的一个重要课题,该文针对时序数据中复杂模式的问题,提出了一种新的时序序列模式的逻辑表示法,并设计出一种新的时序序列建模算法。
    However, for the case of classifying large set and complex patterns, the greater part of conventional neural networks suffer from several difficulties such as the determination of the structure and size of the network, the computational complexity, and so on.
    但是对于识别大样本集和复杂模式的问题 ,绝大多数常规的神经网络在决定网络的结构和规模以及应付庞大的计算量等方面有着种种困难 .
    Experimental results show that this neur al tree is very effective for classification of large set and complex patterns.
    实验显示这种神经网络树对于识别大样本集和复杂模式是非常有效的
    The function-connected neuro-network model is adopted in the evaluation of air quality. It aims to achieve the classification of complex patterns by reinforcing the input patterns,which is different from the conventional BP network.
    在大气环境质量评价中 ,采用了函数连接型神经网络模型 ,与常用的BP网络不同 ,函数连接型网络通过对输入模式的增强来实现复杂模式的分类。
    However, a number of general neural networks have several difficulties such as deciding their structures and scales, designing their self-learning procedure and coping with a bulk of computation for the case of large data set classification and complex patterns recognition.
    但是对于大样本集分类和复杂模式识别问题,大多数常规神经网络在决定网络结构与规模、设计自学习算法和应付庞大的计算量等方面存在诸多困难.
 

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