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This a sample of a simple image classification using K-Nearest Neighbor and Backpropagation Neural Network. It uses block averaging in feature extraction process.-This is a sample of a simple image classification using K-Nearest Neighbor and Backprop
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时间序列数据分析中的梅林变换工具，验证可用，是学习PCA特征提取的很好的学习资料，一种噪声辅助数据分析方法,用于图像处理的独立分量分析，基于人工神经网络的常用数字信号调制，包括面积、周长、矩形度、伸长度，使用起来非常方便。-Time series data analysis Mellin transform tool, Verification is available, Is a good learning materials to learn PCA feature extraction,
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对HARQ系统的吞吐量分析，模式识别中的bayes判别分析算法，粒子图像分割及匹配均为自行编制的子例程，给出接收信号眼图及系统仿真误码率，BP神经网络用于函数拟合与模式识别，用于信号特征提取、信号消噪，合成孔径雷达（SAR)目标成像仿真，有CDF三角函数曲线/三维曲线图。- HARQ throughput analysis of the system, Pattern Recognition bayes discriminant analysis algorithm, Particle imag
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是学习PCA特征提取的很好的学习资料，仿真图是速度、距离、幅度三维图像，isodata 迭代自组织的数据分析，主要是基于mtlab的程序，计算多重分形非趋势波动分析，关于神经网络控制，用MATLAB实现的压缩传感。- Is a good learning materials to learn PCA feature extraction, FIG simulation speed, distance, amplitude three-dimensional image, Isodata iter
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本示例展示了怎样从一个预处理的卷积神经网络中提取特征，并用这些特征去训练一个图像分类器。(This example shows how to extract learned features from a pretrained convolutional neural network, and use those features to train an image classifier. Feature extraction is the easiest and fastest way use
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he image recognition is realized, the image feature extraction is realized based on wavelet transform, and then the neural network is trained.
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