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Hamming 神经网络从功能上来看是最小Hamming 距离分类器．利用它能够完成不完整输入信息与所存储模式的最小汉明距离分类．
Hamming 网络是一个双层神经网络，第一层网（即匹配子网络）是用来计算输入模式与该网络已经学习过的各样本之间的匹配测度．第二层网（即竞争子网络）接收从匹配子网络送来的未知模式与已存各样本的匹配测度，然后经过多次迭代运算就可以求得与输入模式相匹配的样本．-Hamming neural network from the functional point o
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A neural network classifier based on Dempster-Shafer
theory
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A feed forward neural network code for IDL
requres idl licens and at least virtual machine.
Multiple parameters can be set for the neural network classifier
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A feed forward neural network code for IDL
requres idl licens and at least virtual machine.
Multiple parameters can be set for the neural network classifier
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A feed forward neural network code for IDL
requres idl licens and at least virtual machine.
Multiple parameters can be set for the neural network classifier
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A feed forward neural network code for IDL
requres idl licens and at least virtual machine.
Multiple parameters can be set for the neural network classifier
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A feed forward neural network code for IDL
requres idl licens and at least virtual machine.
Multiple parameters can be set for the neural network classifier
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We propose an algorithm for facial expression recognition which can classify the given image into one of the seven basic facial expression categories (happiness, sadness, fear, surprise, anger, disgust and neutral). PCA is used for dimensionality red
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模式识别的图像的贝叶斯分类图图像的贝叶斯分类神经网络模式识别
-Bayesian pattern recognition, image classification maps the image of the Bayesian classifier neural network pattern recognition
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此为类神经网路分类，分类方式为Learning Becttor Quantization 的C语言源码 可直接使用。
-This is a neural network classifier, the classification the Learning Becttor Quantization of the C language source code can be used directly.
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BP神经网络分类器程序源码有两种运行状态，一个是学习，另外一个是分类。在学习状态下，在Dos命令符下输入bp learn，便开始学习了，学习的结果放在weight.dat中；在工作状态下，在Dos命令令符下下输入bp work，便开始识别classfyme.dat中的数据了，识别完成后，结果放在results.dat中。在bp运行的任何一种状态下，都不能手工打开Weight.dat、Sample.d
-BP neural network classifier program source c
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BP神经网络作为若分类器，反复训练BP神经网络预测样本输出-As if the BP neural network classifier, repeated training samples of BP neural network output
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应用神经网络与神经网络相关的几个问题,这包裹集中经典分类器,和图像分割,特别是彩色图像分割和过滤-Application of neural networks is associated with several issues related to the neural network, which wrapped centralized classical classifier, and image segmentation, especially color image segmentati
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SVM神经网络中的参数优化 -如何更好的提升分类器的性能 绝对可以无错运行-SVM neural network classifier parameter optimization performance improvement - how to better the absolute can be error free operation
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SVM神经网络中的参数优化 -如何更好的提升分类器的性能 绝对可以无错运行-SVM neural network classifier parameter optimization performance improvement - how to better the absolute can be error free operation
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SVM神经网络中的参数优化 -如何更好的提升分类器的性能-SVM neural network classifier parameter optimization performance improvement- how the better
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A feed forward neural network code for IDLrequres idl licens and at least virtual machine.Multiple parameters can be set for the neural network classifier
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A feed forward neural network code for IDLrequres idl licens and at least virtual machine.Multiple parameters can be set for the neural network classifier
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本内容是有关机器学习的包含贝叶斯分类器，随机森林，支持向量机，神经网络，logistic多元回归等(The contents of this paper are machine learning, including Bayesian classifier, random forest, support vector machines, neural network, logistic multiple regression and so on)
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态势要素获取作为整个网络安全态势感知的基础，其质量的好坏将直接影响态势感知系统的性能。针对态势要素不易获取问题，提出了一种基于增强型概率神经网络的层次化框架态势要素获取方法。在该层次化获取框架中，利用主成分分析(PCA)对训练样本属性进行约简并对特殊属性编码融合处理，将其结果用于优化概率神经网络(PNN)结构，降低系统复杂度。以PNN作为基分类器，基分类器通过反复迭代、权重更替，然后加权融合处理形成最终的强多分类器。实验结果表明，该方案是有效的态势要素获取方法并且精确度达到95.53%，明显优于
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