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于 2017-04-10 发布 文件大小:12KB
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  2采用PCA对男女生样本数据中的身高,体重,喜欢数学,喜欢文学,喜欢运动,喜欢模式识别共6个特征进行特征提取(自己设定选取的特征个数),并基于所得到的特征采用SVM设计男女生分类器,并计算模型预测性能(包含SE,SP,ACC和AUC )。(2 using PCA for boys and girls in the sample data height, weight, like math, like literature, like sports, like common pattern recognition feature extraction feature 6 (wherein the number of the selected set itself), based on the obtained feature using SVM classifier design boys and girls, and calculating prediction performance model (including SE, SP, ACC and AUC).)

文件列表:

code
....\PCA.m,1624,2016-10-29
....\PCA.xlsx,14768,2016-10-26

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    根据输入的电路矩阵表示形式,形成节点导纳矩阵,形成因子表的过程可以看做是规格化运算和消去运算的重复过程,重复的次数和非基准点的节点个数相同。 每个过程中进行的操作都相同,首先,存储规格化因子及消去因子;其次,对本行进行规格化运算,并将其值存储在因子表中;然后,对其后的每行进行消去运算;如此循环n次得到因子表。(Representation of the input of the circuit matrix, forming a node admittance matrix formation process can be seen as a factor table normalization process operations and eliminate duplicate operations, the same number of nodes and the number of repetitions of non-reference point. Each operation carried out during the same, first of all, storage and elimination factor normalization factor secondly, the Bank normalized operations, and its value is stored in the factor table then, each row will be erased after their operation so the cycle n times to get factor table.)
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