The correct answer is: B. Gaussian RBF kernel.
A kernel is a function that maps data points from a lower-dimensional space to a higher-dimensional space. In support vector machines (SVMs), the kernel is used to calculate the similarity between data points. The Gaussian RBF kernel is a popular choice for SVMs because it is relatively simple to implement and it often performs well.
The linear kernel is a special case of the Gaussian RBF kernel where the width of the Gaussian is set to zero. This means that the linear kernel does not map data points to a higher-dimensional space; instead, it simply calculates the dot product of the two data vectors. The linear kernel is often used when the data points are linearly separable.
The polynomial kernel is another popular choice for SVMs. It is defined as the dot product of the two data vectors raised to a power. The polynomial kernel can be used to map data points to a higher-dimensional space, which can improve the performance of SVMs on non-linearly separable data.
In conclusion, the Gaussian RBF kernel is the most appropriate kernel that can be used with SVM to separate the classes. It is relatively simple to implement and it often performs well.