I'm currently studying machine learning (support vector machines to be more specific), and I was wondering how exactly I should understand what a kernel function is. I've read other questions on this community such as:
- How to intuitively explain what a kernel is?
- Understanding kernel functions for SVMs
- What function could be a kernel?
However, I'm still having trouble grasping the concept and was hoping somebody would be able to help me out.
My initial understanding is that a kernel is essentially just a mapping into a higher dimension. For example, when we want to make better predictions using a linear classifier, we would use a kernel to map the decision boundary to a higher dimension and make better predictions.
Is my understanding at least on the right track?
Any feedback is appreciated. Thank you.