The weight update rule in (1.3) has the nice interpretation that it movesin the direction of classifying x(t) correctly.(a) Show that y(t)w^T(t)x(t) y(t)w(t)x(t). [Hint: Use (1.3).](c) As far as classifying x(t) is concerned, argue that the move from w(t)to w(t 1) is a move 'in the right direction'.(1.3)The update rule is w(t 1) = w(t) y(t)x(t).This rule moves the boundary in the direction of classifying x(t) correctly, asdepicted in the figure above. The algorithm continues with further iter
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