机器学习
在实践中，SVM 算法通过核函数实现，核函数将输入数据空间转换成所需的形态。SVM 使用一种称为核技巧的技术，核技巧将低维输入空间转换为高维空间。简单来说，核函数通过增加更多维度，将不可分问题转换为可分问题。这使得 SVM 更强大、更灵活、更准确。以下是 SVM 使用的一些核函数类型：
线性核 (Linear Kernel)
线性核可以作为任意两个观测值之间的点积。线性核的公式如下：
从上面的公式可以看出，两个向量 x 和 xi 之间的乘积是每对输入值相乘后的总和。
In practice, SVM algorithm is implemented with kernel that transforms an input data space intthe required form. SVM uses a technique called the kernel trick in which kernel takes a low dimensional
input space and transforms it into a higher dimensional space. In simple words, kernel converts non-
separable problems into separable problems by adding more dimensions to it. It makes SVM more
powerful, flexible and accurate. The following are some of the types of kernels used by SVM.
Linear Kernel
It can be used as a dot product between any two observations. The formula of linear kernel is as below
K(x,xi)=sum(x∗xi)
From the above formula, we can see that the product between two vectors say x & xi is the sum of the
multiplication of each pair of input values.