There's 150 samples total and I extracted 135 of them,

and plotted them to look

for similarities in the features.

You can see they clustered quite nicely.

The Setosa's are way up over here and a group by themselves,

and the Versicolor and the Virginica's still separate pretty well.

You can imagine a plane passing through there.

So, there is some structure in the data set.

Without all the math,

it's just sort of a summary comment here.

A linear support vector machine is a type of classifier where

the features fall into one of several categories.

That's my little cartoon example there with two categories shown here.

This happens in multiple dimensions.

Andrew, referred to this as a hyperplane,

but as we'll see in an example in a minute,

that can be curved surfaces as well.

Potentially complex curved surfaces that divide the classes,

locations of feature sets one from another.

It turns out that

these features that are close to this hyperplane that separates the categories,

are what are called the support vectors.

So, here's yet another library of learning algorithms.

This is live at SVM.

Go up by the Skype person.