So I am working on a linear perceptron algorithm problem that has 3 training samples. (2D space)
x1 = (1,3) class 1 (y1 = 1) x2 = (3,2) class 2 (y2 = -1) x3 = (4,1) class 2 (y2 = -1)
and the linear perceptron is initialized with a line with corresponding weight
w(0) = [2,-1,1]^T = 2 - x + y = 0
What I am confused by is how to use the weight . update rule to find the new weight w(1) based on the misclassified initial sample.