You are given a single-layer perceptron with two inputs x1 and x2, and one threshold corresponding to a fixed input denoted as x0 = -1. The perceptron is based on a signum activation function of output values +1 (for non-negative input) and -1 (for negative input). At some point, during the task of training, you observe your network to have weights: w0 = +1.5 (the threshold), w1 = +2.5, w2 = +0.4 (weight indices correspond to input indices). Then, you need to train for pattern (x1, x2) = (1, 0) with desired outcome d = -1, and adjust the weights accordingly, with a learning rate of 0.5. What will those weights become as soon as you train for this pattern?
(a) Correct Answer: (+2.5, +1.5, +0.4)
(b) (+2.5, +1.5, +1.4)
(c) (+2.5, +2.5, +0.4)
(d) (+3.5, +1.5, +0.4)