I'm new to the AI Stackexchange and wasn't certain if this should go here or to Maths instead but thought the context with ML may be useful to understand my problem. I hope posting this question here could help another student learning about Support Vector Machines some day.
I'm currently learning about Support Vector Machines at university and came across a weird step I could not understand. We were talking about basic SVMs and formulated the optimisation problem $\max_{w,b} \{ \frac{1}{||w||} \min_n(y^{(n)}f(x^{(n)}))\}$ which we then simplified down to $\max_{w,b} \{ \frac{1}{||w||}\}$ by introducing $\kappa$ as a scaling factor for $w$ and $b$ according to the margin of the SVM. Now our lecturer converted it without explanation into a quadratic optimisation problem as $\min_{w,b}\{\frac{1}{2} ||w||^2\}$ which I could not explain myself. I hope someone with context can help me how this is possible and what math or trick is behind this approach?
Notation information:
- $w$ - weight matrix
- $b$ - bias (sometimes denoted $w_0$ I believe?)
- $x^{(n)}$ - Independent variable (vector)
- $y^{(n)}$ - Dependent variable (scalar classifying the input in a binary classifcation as $y=1$ or $y=-1$)
Thank you very much!