) ) ) , = = Given the following samples from two classes: W1:{(-1,0),(0, -1),(1,0),(0,1))} and wz:{(-2,0)",(0, 2)", (2,0)",(-2, 2)"}. 2 The class label for classes wi and wz is r1 = 1 and r2 = -1, respectively. Each W1 ri sample is denoted as (x1, x2). The equation of the boundary used to separate the samples is expressed as w1x1 + w2x2 + b = 0. (a) Are the classes linearly separable? (b) Apply (i) the perceptron algorithm, and (ii) the LMS algorithm, to the pattern classes, with a learning rate of 0.1. The initial boundary is given as x1 = x2. The number of epochs is 2. Determine the classification accuracy after each epoch. = ) ) ) , = = Given the following samples from two classes: W1:{(-1,0),(0, -1),(1,0),(0,1))} and wz:{(-2,0)",(0, 2)", (2,0)",(-2, 2)"}. 2 The class label for classes wi and wz is r1 = 1 and r2 = -1, respectively. Each W1 ri sample is denoted as (x1, x2). The equation of the boundary used to separate the samples is expressed as w1x1 + w2x2 + b = 0. (a) Are the classes linearly separable? (b) Apply (i) the perceptron algorithm, and (ii) the LMS algorithm, to the pattern classes, with a learning rate of 0.1. The initial boundary is given as x1 = x2. The number of epochs is 2. Determine the classification accuracy after each epoch. =