But it turns out that for some learning problems, what you really want is not to use, like, three or five features.

Let's say we're using the tumor classification example.

You may notice they're wearing very thick gloves.

The perfect desert machine. I'm smartly designed.

Anything else?

Any description?

That sure narrows it down.

Can you give me a description of the car, sir?

Other features

Okay, what does your husband look like?

How will I recognize the person I'm meeting?

We saw left feature number 1.

Scrums are a feature of Rugby football.

So what are these characteristics that I'm talking about?

There'sbeen a massive explosion in midcity washington.

I mean, we've all seen apish people.

panning which means putting the shaped loaf on a pan.

Well, we sent out an all points.

It's just the female.

So why don't we leverage that? And that's why we built this.

DiNozzo. How many cabdrivers in DC fit this general description?

Match the rest against our victims' stats.

This is an image I took a while back in Amsterdam, and it's interesting because there's a lot of interesting features

They really loved it.

But you've never seen this one before.

And he was really gagging. I thought he might throw up on me, actually.

That was characteristic.

What else? Aside from they hear you?

And you should observe, that the perceived order of features in the scene, by virtue of using a pinhole, will be inverted inside the pinhole camera.

It doesn't matter whether it's a poem or whether it's a mathematical text.

And they have different properties.

Great speed is a feature of the Concorde.

Describe the level characteristics

Suppose in camera A we see from left to the right the following sequence 1, 2, 3.

Young. Well, he moved Like he was young.

My own website.

Well, and you're glad I didn't show you this.

Characteristic of a fourfooted animal.

What about other distinguishing features?

found two nights ago at the Pier?

And it turns out one of the interes , most interesting learning algorithms that we'll see in this class is a learning algorithm that can deal with, not just two or three or five features, but an infinite number of features.

And so that explains this pattern that we've seen before.

For a different configuration let's now assume we get to see 1, 2, 3 in camera B, and we care about the feature indices we see in camera A that corresponds to those features over here.

They did report one distinctive feature which bears mentioning.

By combining both of them, we get a gradient s l magnet? image, and we have our very first feature detector, which is a feature detector of any edge in the image.