In class, the perceptron update rule was defined as: . * On a correct prediction, do nothing. * On a false positive prediction, set @BT@vec{w} := vec{w} - vec{x}@BT@ and set @BT@theta := theta + 1@BT@. * On a false negative prediction, set @BT@vec{w} := vec{w} + vec{x}@BT@ and set @BT@theta := theta - 1@BT@. . In contrast, for the Winnow update rule, it says: . * On a correct prediction, do nothing. * On a false positive prediction, for all @BT@i@BT@, set @BT@w_i := alpha^{-x_i}w_i@BT@. * On a false negative prediction, for all @BT@i@BT@, set @BT@w_i := alpha^{x_i}w_i@BT@. . My assumption would have been that @BT@\theta@BT@ gets updated the same for both the Winnow and Perceptron update rules. Is this an incorrect assumption?

-- HW01: Winnow Update Rule

For this update rule, both @BT@alpha@BT@, and @BT@theta@BT@ are hyperparameters. I.e., you set and forget them.