I'm struggling to solve this past paper question and my lecturer is being less than helpful. The question is:
Apply the perceptron learning rule to update the current weight vector (0.1, 0.3) when the learning rate is 0.01, the input vector is (2.0, 4.0), and the error vector is (1, -1).
What has me confused is the use of vectors for the current weight, input and error value. We've never used vectors for this before (only regular numbers) and no examples I can find online use 2D vectors like this.
I know the perceptron learning rule is:
New weight = old weight + (learning rate * perceptron input * error value)
But I'm not sure how to do this kind of maths with 2D vectors to get a 2D vector as my result (which is what I assume I want, since the question seems to be asking for the new weight).