Final due Sep $9,202419$:$59$ WITA

Consider a classification problem where we are given a training set of $n$ examples and labels

$S_n=\{(x^{(i)},y^{(i)})$:$i=1,$dots,$n\},$ where $x^{(i)}in{R}^2$ and $y^{(i)}in\{1,-1\}.$

Assume a different data set for the two problems below.

$(7)$

$2\mathrm{.}5$ points possible $($graded$,$ results hidden$)$

Consider a elassification problem where we are given training set of nexamples and labels

suppose for a moment that we are ableto find a linear classifier with parameters $$ and $$ such that

The minimization problem definedby the equation immediately above has a solution if and only if the

training examples Sn are linearly separable.

True

False

The training examples Si are linearly separable under our assumptions.

False

True

False

$({}^{\text{'}}-x^{(i)},{}^{\text{'}})((hat())-x^{(i)},$hat$({)}_0)$ for alli $1,$dots,$rz.$

longleftrightarrow True

longrightarrow False

$||{}^{\text{'}}||||hat()||.$

True

False