When we discussed the Perceptron Learning Algorithm $($PLA$)$ we updated the weights using the rule w$($t $+1)=$ w$($t$)+$ y$($t$)$x$($t$).$ This rule moved the Perceptron in the direction of classifying x$($t$)$ correctly.

A$.$ What were to happen if we moved the highlighted term $[$y$($t$)]$ in red to the right as shown here x$($t$)$y$($t$)?$

B$.$ What were to happen if we removed the highlighted term in red $[$y$($t$)]$ as shown here x$($t$)$y$($t$)?$

C$.$ Mathematically show that y$($t$)$w$($t$)$x$($t$)<0$