A concept in Supervised learning

Intuition of geometric margins – definition of geometric margin, and derivation

The formula is

$\gamma^{(i)} = y^{(i)}[(\frac{\omega}{||\omega||})^T x^{(i)} + \frac{b}{||\omega||}]$

It is very similar to Functional margin, and in fact equal if $omega = 1$.

W.r.t. an entire training set , the geometric margin is the min of thec$v$ ofver all data points $i$ inn the training set.