HW2Due on March 13Support vector regression (SVR) extends SVM to regression problem with a similar formulation of minimizing \(\w\^2\) subject to margin constraints. For softmargin SVR, a typical formulation of fitting \(y=f({\bf x})\) with training data \({\bf x}_1,\cdots,{\bf x}_l\) and \(y_1,\cdots,y_l\) is as follows (c.f. (3) in this tutorial) \(\min \frac{1}{2} \{\bf w}\^2 + C \sum_{i=1}^l (\xi_i+\xi_i^*)\) s.t. \(\begin{cases} y_i  \langle {\bf w}, {\bf x}_i \rangle b \le \epsilon +\xi_i \\ \langle {\bf w}, {\bf x}_i \rangle +b y_i \le \epsilon +\xi_i^* \\ \xi_i, \xi_i^* \ge 0 \end{cases}\)
