The prior SVMs worked with linear separable data. If we separate non-linear data, we can change how we project the linear separator onto the data. This is done by changing the kernel in the SVM loss function. In this chapter, we introduce how to change kernels and separate non-linear separable data.

# Working with kernels in TensorFlow

# Getting ready

In this recipe, we will motivate the usage of kernels in Support Vector Machines. In the linear SVM section, we solved the soft margin with a specific loss function. A different approach to this method is to solve what is called the dual of the optimization problem. It can be shown that the dual for the linear SVM problem is given by the following formula:

To this, the following...