Quadric Machine Homework Assume a 2 input perceptron expanded to be a quadric perceptron (it outputs 1 if net > 0, else 0). Note that with binary inputs of -1, 1, that x2 and y2 would always be 1 and thus do not add info and are not needed (they would just act like two more bias weights) Assume a learning rate c of .4 and initial weights all 0: Aw; = c(t z) x; Show weights after each pattern for one epoch with the following non-linearly separable training set (XOR). Has it learned to solve the problem after just one epoch? Which of the quadric features are actually needed to solve this training set? Target 0 -1 -1 -1 1 1 1 -1 1 1 1 0 CS 472 - Homework 4 Quadric Machine Homework Assume a 2 input perceptron expanded to be a quadric perceptron (it outputs 1 if net > 0, else 0). Note that with binary inputs of -1, 1, that x2 and y2 would always be 1 and thus do not add info and are not needed (they would just act like two more bias weights) Assume a learning rate c of .4 and initial weights all 0: Aw; = c(t z) x; Show weights after each pattern for one epoch with the following non-linearly separable training set (XOR). Has it learned to solve the problem after just one epoch? Which of the quadric features are actually needed to solve this training set? Target 0 -1 -1 -1 1 1 1 -1 1 1 1 0 CS 472 - Homework 4