Through SVM algorithm, solving the building problem of input sample feature vector (weak information sample) in the process of extracting mineralizing information from RS data.
解决了应用SVM识别算法对遥感矿化信息提取过程中输入样本特征向量（微弱信息样本）的构造问题。
According to the method, the energy of different frequency bands after wavelet packet decomposition constitutes the input vectors of support vector machine as feature vectors.
该方法将振动信号小波包分解后的频带能量作为特征向量，输入到由多个支持向量机构成的多故障分类器中进行故障识别和分类。
Optimization of feature selection, we select area, perimeter, long axis, short axis, Euler number, geometric moments, and a total of 12 feature parameters as input vector.
优化特征选择，选定面积、周长、长轴、短轴、欧拉数、几何矩等共12个特征参数作为神经网络输入向量进行分类试验。
Experimental results show that the feature extraction approach remarkably reduces the dimensionality of the input vector while the characteristics of the signals have been reserved.
实验结果表明，该信号测量和抽取算法在有效保留信号精度的同时，显蓍地减少了信号特征向量的维数。
Experimental results show that the feature extraction approach remarkably reduces the dimensionality of the input vector while the characteristics of the signals have been reserved.
实验结果表明，该信号测量和抽取算法在有效保留信号精度的同时，显蓍地减少了信号特征向量的维数。
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