I'm going to sketch a

So I hope that gives some intuition about how this support vector machine functions as a large margin classifier that tries to separate the data with a large margin, technically this picture of this view is true only when the parameter C is very large, which is a useful way to think about support vector machines.

Now, the SVM is actually sligthly more sophisticated than this large margin view might suggest.

If he had one example then it seems as if to separate data with a large margin, maybe I'll end up learning a decision boundary like that, right? that is the magenta line and it's really not clear that based on the single outlier based on a single example and it's really not clear that it's actually a good idea to change my decision boundary from the black one over to the magenta one.

That is, trying to separate the positive and negative examples with as big a margin as possible.

There is no free market for oil.

Left margin

Right margin

Top margin

Bottom margin

And so this picture of a large margin classifier that's really, that's really the picture that gives better intuition only for the case of when the regulations parameter C is very large, and just to remind you this corresponds

like this, maybe we'll choose that decision boundary that separate the positive and negative examples on large margin.

It draws your text bold, a little larger with a little margin around it, and that's it.

Customize gaps and margins

What are your operating costs? What are the basic fixed and variable costs of the business?

This is a pretty profitable business, more profitable than most.

Sometimes people talk about support vector machines, as large margin classifiers, in this video I'd

I'm going to make the text of our submit button bigger.

It's actually quite entertaining.

People are willing to invest more, and the budgets are larger.

So let's check again.

is greater than

Greater Than

greater than

greater

And so this distance is called the margin of the support vector machine and this gives the SVM a certain robustness, because it tries to separate the data with as a large a margin as possible.

For example, for the politician, the strategy of contracting is domininated by the strategy of doing nothing.

We borrowed from this 3, which becomes a 2.

I can't wait to see our planet. It's bigger than this one, isn't it?

And it all connects us together into something that is much bigger and much faster and able to evolve than what we were before.

So normally if XT and XT plus one is the same, this will be zero.

But anyway, phi ... is more than one, but less than two more than 3 2, less than 5 3 greater than 8 5, but 13 8 is too big.

If it's not well, now we've got the case where a was greater than b . a is not greater than c , so that means c is greater than or equal to a .

So now we say 8 is larger than 7.

This quantity right here has to be between negative 2 and 1 2.

And this 6 will become a 5.

Because if x is greater than 1, then x is definitely going to be greater than negative 2.

So the support vector machine is sometimes also called a large margin classifier and this is actually a consequence of the optimization problem we wrote down on the previous slide.

greater than

Larger Than

You are so great. I owe you big time.

For the greater good.

I outgrew them.

That's where you put the larger number.

It affects your sales model. Are you going to hire direct sales? When do you hire them?