Hello and welcome back to the course of Machine Learning.
Previously we talked about the Support Vector Machine algorithm.
And today we're going to talk about the kernel support Vector machine algorithm.
And the intuition behind it.
So as you recall in the support vector machine situation,
we had a set of observations, which belong to different classes.
And the point was to find this decision
boundary between them so that any future observations
could be identified which class they fall into.
And in this case, we can see that, there's a decision boundary.
And the support vector
machine algorithm tells us exactly how to find that boundary.
But what happens when we cannot find a boundary?
What happens in a situation like this, for example.
What what do we do here?
We can't just draw a line through these points, right?
We can't just separate them with a straight line.
We can't separate them with a horizontal line.
We can't separate them with a vertical line.
Whatever we try to do, we cannot separate these points
in the same way that the support vector machine algorithm, told us to.
And so what happens in this situation and why does it happen?
Well, this happens because in this case,
the data is not linearly separable.
So here we've got the two examples side by side on the left
the linearly separable data.
And on the right the non-linearly separable data.
And what's the support vector machine algorithm does is it
helps us find that decision boundary
or correctly place that decision boundary.
But it does have an assumption.
The assumption is that the data is linearly separable.
So basically it's saying that it is possible to place the decision boundary.
So support vector machine algorithm helps us choose the optimal
decision boundary out of the multiple decision boundaries that we can draw.
Whereas in the non linearly separable case we can't even draw one
single decision boundary or linear decision boundary.
So therefore the support vector machine algorithm just won't work by definition.
And so what are we going to do.
Well this is what this section is about.
This section is about coming up with an algorithm that will help us deal
with situations like that where we have to extract this class,
when it is surrounded or,
in other situations where you can't just simply just draw a line.
And the way this section is structured or the intuition tutorials of this section
is, first of all, we're going to explore a method which is called,
going to a higher dimensional space where we will learn how we can take,
this data set and add an extra dimension
into our, space that we're dealing with
and make our data, linearly separable.
So we'll have a look
at a couple of those examples, and then we will discuss the kernel trick,
which allows us to do these things in a more computationally efficient way
without having to deal with multiple, or higher dimensions.
And finally, we will talk about the different types of kernels that exist.
So quite an interesting section ahead, and I can't wait
to see you in the next tutorial. Until then, happy analyzing.