Consider designing a linear binary classifier \( f(x) = \text{sign}(w^T x + b), x \in \mathbb{R}^2 \) on the following training data: Class-2: \( \left\{ \left( \begin{array}{c} 0 \\ 0 \end{array} \right) \right\} \) Hard-margin support vector machine (SVM) formulation is solved to obtain \( w \) and \( b \). Which of the following options is/are correct?
If rank(A) is at least 3, then what are the possible values of \( \alpha, \beta, \gamma \)?
The variance for continuous probability function \(f(x) = x^2 e^{-x}\) when \(x \ge 0\) is