Consider a Perceptron whose output is given by h(wo+w1×1+W2X2), where x1, X2 are inputs and h() is the Heaviside (step) function. Assume this Perceptron is being trained on the data in the following...

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Extracted text: Consider a Perceptron whose output is given by h(wo+w1×1+W2X2), where x1, X2 are inputs and h() is the Heaviside (step) function. Assume this Perceptron is being trained on the data in the following table, and that the current values of the weights are wo= -0.5, W1= -1 and w2= -2. Training Examplex1 x2 Class |-1-1 Neg 2 1 Neg (a) (b) (c) |-2 2 Pos If the Perceptron Learning Rule is applied to the current weights, using training item (a) and a learning rate of n=1.0, the new values for wo, W1 and w2 at the end of this training step will be: * W: