ributes to the loss, so each data point has an effect on the solution.
That's also true for all the regression models we saw last time.

---


# (soft margin) linear SVM

.larger[
`$$\min_{w \in ℝ^{p}}C \sum_{i=1}^n\max(0,1-y_iw^T \textbf{x}_i) + ||w||_2^2$$`

`$$\min_{w \in ℝ^{p}}C \sum_{i=1}^n\max(0,1-y_iw^T \textbf{x}_i)+ ||w||_1$$`
]

- Both versions strongly convex, neither smooth.

- Only some points contribute (the support vectors) to $w$ (sparse solution to dual).

???
Moving from logistic regre