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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. Show that y(t)w(t)x(t) < 0
B. Show that y(t)w(t+1)x(t) > y(t)w(t)x(t)
C. Why would w(t+1) = -y(t)x(t)+w(t) not be in the correct direction?
Give a constructive argument.
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