Hello and welcome back to the course on Machine Learning.
I hope you enjoyed the previous tutorials.
And today we're going to talk about the different types of kernel functions.
So the final thing that you need to know about the kernel SVM is that the radial
basis function, which is also called the Gaussian function,
is a not the only kernel function that is used in this method.
so let's have a look at a couple.
So here we've got the Gaussian or the RBF kernel, which we've already spoken about.
Then there's also a popular choice which is a sigmoid kernel.
And that's the formula on the right.
It's in a bit of a different notation, but the essence is the same is that you
still select a, landmark and then,
then from there, depending on the distance,
the landmark, different results will occur.
But in this case, as you can see, this kernel function is directional.
So anything, even like just looking at it in this, two
dimensional space is just a projection.
You can see that anything to the right will, be automatically right away.
will have a high value.
So it will be included in your classification.
Anything to the left will be excluded.
So sometimes you might require these situations.
So maybe if you look at a two dimensional space, just imagine those points
that we had points on on one side are not in your classification.
On the other side, are you in your classification.
You want to somehow, outline
that decision boundary, or, highlight
that as, points past a certain points are should be in your classification.
Then a sigmoid kernel is a popular choice.
Also, we've got, polynomial kernels, which are also popular.
one of the more popular choices.
And, here you can have, like, a, polling them,
which dictates how your kernel behaves.
And, again, you can go into more detail on that.
So this tutorial is not for us to discuss
the specifics of each one of these, kernels, but
just to, show you that they exist and, help you be aware of the different types.
And so these are the most popular choices the Gaussian, RBF,
sigmoid and polynomial.
Hope you enjoyed,
learning about the kernel SVM and I look forward to see you next time.
Until then, happy analyzing.