Primal Problem Given a linear separable training set D = {(x1, y1), (x2, y2), ..., (xl, yl)} ⊂ Rn × {+1, −1}, we can calculate the max margin decision surface ⟨w∗, x⟩ = b∗ solving the convex program (P) min w,b ϕ(w, b) = 1 2 ⟨w, w⟩ subject to ⟨w, yixi⟩ ≥ 1 + yib, where (xi, yi) ∈ D ⊂ Rn × {−1, +1}. (1) 1. The objective function doesn’t depends on b. 2. The displacement b appears in the restrictions. 3. The number of restrictions is equal to the number of training points. 12 / 35