Smoothing

Smoothing

Edge Smoothing

Image Smoothing

like this, after the smoothing.

They're smoothing out.

Just as in the Laplacian smoothing example before, the value of k is a smoothing parameter.

Well, you might remember the work on Laplacian smoothing.

Such a method is called input smoothing.

There's many different solutions, but a common one could be to use smoothing in a different way from the way we discussed it before.

like the Laplace smoothing that we talked about, and there are many other techniques for doing smoothing to come up good estimates.

This value controls the smoothing effect of the pencil under the canvas.

This value controls the smoothing effect of the brush under the canvas.

I really want you to remember Laplacian smoothing.

And therefore we need some type of smoothing,

In Laplace Smoothing, we use a different estimate.

Thrun So in Laplacian smoothing we look at the relative counts.

What is p (spam) for the Laplace smoothing of k 1?

It is Laplacian smoothing, which can be applied just exactly the same way we saw it in class in various contexts.

Well, the prior probability for SPAM under the Laplacian smoothing is 2 5.

One technique to deal with the overfitting problem is called Laplace smoothing.

Thrun This question is about naive Bayes and Laplacian smoothing.

Please use Laplacian smoothing for this with K equals 1.

Smoothing was the only planner that actually really worked in a continuous domain.

If we change this to no smoothing weight, we obviously get the original path.

So let's not assume we have Laplacian smoothing and instead use the maximum likelihood estimator.

However I'd like to do this with Laplacian smoothing with k 1.

Interestingly, the maximum likelihood on the last 2 cases over here will give us .6, but we only get a value that's closer to .5, which is the effect of our smoothing prior for the Laplacian smoothing.

Again, that depends on exactly what type of smoothing you choose to do.

Today I only have programming assignments. One on smoothing and one on control.

We just look at those counts and apply whatever smoothing we decided is best.

But if you understood Laplacian smoothing, you might want to give it a try.

This is a cyclic course so we're smoothing before what's for an open path.

In this question, we apply naive Bayes with Laplacian smoothing the same as we have learned in class.

It turns out that in practice the smoothing techniques that we use are a little bit more sophisticated.

He'll lift you way, way, way, way, way into the air.

I'd now like you to modify smoothing, so it can smooth this path over here.

In Laplacian smoothing, we should increase k because we smooth more, which will accomodate the problems introduced by the additional noise.

Or would it be helpful to do a better job with our smoothing algorithm? Check here.

Leading the way what's leading the way?

And the way they move? The way...

Clear the way. Clear the way, men.

I recommend you use a single dictionary for smoothing, so look at all the words and see how large the dictionary is.

I had a PhD student work on very narrow openings with smoothing where we used the distance between obstacles as one way to scale the pressure that obstacles would assert on the path to pull away from themselves.

now 0.003, and nowi doesn't occur at all, and so we'd use some factor based on smoothing.