SVC and NuSVC are similar methods, but accept slightly different sets of parameters and
have different mathematical formulations (see section Mathematical formulation).
On the other hand, LinearSVC is another implementation of Support Vector Classification for the case of a linear kernel.
Note that LinearSVC does not accept keyword kernel,
as this is assumed to be linear. It also lacks some of the members of SVC and NuSVC, like support_.
SVC/NuSVC是比较类似的,但是在调用时,所接受的参数是略有不同的,不同的部分详见数学公式
另外一方面,LinearSVC是一个实现带有线性核函数的支持向量机分类器。
注意: LinearSVC是不接受keyword kernel,因为这是被假设认为是线性的。
同时,LinearSVC也缺少像SVC/NuSVC中的一些属性成员,比如 support_
As other classifiers, SVC, NuSVC and LinearSVC take as input two arrays:
an array X of size [n_samples, n_features] holding the training samples,
and an array y of class labels (strings or integers), size [n_samples]:
和其他的分类器一样,
SVC/NuSVC/LinearSVC 将两个数组作为输入,分别是:二维向量(矩阵)X[n_samples, n_features]和一维向量(矩阵)Y [n_samples]。