what?

一种分类的方法，Largest Margin,

maxwmin_i(WXi+b)

最大的Margin

why？

支持向量的名字的来源

The points closest to the separating hyperplane are known as support vectors(最接近超平面的点）

Margin: Distance of closest example from the decision line/hyperplane

make our classifier in such a way that the farther a data point is fromthe decision boundary, the more confident we are about the prediction we’ve made.

离决策边境越远越confident,更容易确认到底是1 or -1

We want to have the greatestpossible margin, because if we made a mistake or trained our classifier on limiteddata, we’d want it to be as robust as possible

Create an alphas vector filled with 0s

 While the number of iterations is less than MaxIterations:

   For every data vector in the dataset:

     If the data vector can be optimized:

       Select another data vector at random

       Optimize the two vectors together

       If the vectors can’t be optimized ➞ break

   If no vectors were optimized ➞ increment the iteration count