Smoothing

Smoothing

Edge Smoothing

Image Smoothing

like this, after the smoothing.

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

Such a method is called input smoothing.

I really want you to remember Laplacian smoothing.

And therefore we need some type of smoothing,

In Laplace Smoothing, we use a different estimate.

Well, you might remember the work on Laplacian smoothing.

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

Thrun This question is about naive Bayes and Laplacian smoothing.

Please use Laplacian smoothing for this with K equals 1.

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

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

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

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

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

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

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

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

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

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.

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

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

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

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

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

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.

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

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.

Here is seems to be a problem of not enough data and not a very good smoothing algorithm.

You might remember all this work with Laplace smoothing, but for now we keep it simple, just maximum likelihood.

If we think of this differential CTE term as a sort of smoothing term, which damps out these wild oscillations.

One thing we talked about before, when we were doing the spam filterings and so on, is a necessity of smoothing.

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

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

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

We're going to have to do more smoothing, more guessing what those probabilities are, because we just won't have the counts for them.

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.

So I'm going to ask you a quiz here, even though I haven't completely shown you the application of Laplacian smoothing in this context.