2 Domain Adaptation Support Vector Machines [20pts] We now look at a different type of SVM that is designed for domain adaptation and optimizes the hyperplanes given by ws (source hyperplane) before optimizing wr (target hyperplane). The process begins by training a support vector machine on source data then once data from the target are available, train a new SVM using the hyperplane from the first SVM and the data from the target to solve for a new "domain adaptation" SVM. The primal optimization problem is given by n arg min |will2 + C) Si - BWTWS WT,S i=1 s.t. yi(WTXi + b) 21 - Si Vie {1, ...,n} Si 20 Vie {1,..., n} where ws is hyperplane trained on the source data (assumed to be known), wT is hyperplane for the target, yi E {41} is the label for instance xi, C & B are regularization parameters defined by the user and & is a slack variable for instance xi. The problem becomes finding a hyperplane, wr, that minimizes the above objective function subject to the constraints. Solve / derive the dual optimization