Trace the Perceptron algorithm for the following input. Suppose the actual labels are with respect to a line passing through the origin with normal vector w* = (-1, 1); for example, the true label of (1,0) is '-1' and that of (0,1) is '+1'. The points are ₁ = (0, -1), x2 = (-1,0), x3 = (2,0), x4 = (√√2,1), x5 = (-3,-2). You need to follow the same assumptions as in the example in Slide 6 of Module 10. Show your work and specify the number of mistakes by the algorithm. x5: (-3,-2) 24=(√2,1) 22: (-1,0) xy: (2,0) (0,-1)
Trace the Perceptron algorithm for the following input. Suppose the actual labels are with respect to a line passing through the origin with normal vector w* = (-1, 1); for example, the true label of (1,0) is '-1' and that of (0,1) is '+1'. The points are ₁ = (0, -1), x2 = (-1,0), x3 = (2,0), x4 = (√√2,1), x5 = (-3,-2). You need to follow the same assumptions as in the example in Slide 6 of Module 10. Show your work and specify the number of mistakes by the algorithm. x5: (-3,-2) 24=(√2,1) 22: (-1,0) xy: (2,0) (0,-1)
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Transcribed Image Text:Trace the Perceptron algorithm for the following input. Suppose the actual labels are with respect to a line passing through
the origin with normal vector w* = (-1, 1); for example, the true label of (1,0) is '-1' and that of (0,1) is '+1'.
The points are ₁ = (0, -1), x2 = (-1,0), x3 = (2,0), x4 = (√√2,1), x5 = (-3,-2). You need to follow the same
assumptions as in the example in Slide 6 of Module 10. Show your work and specify the number of mistakes by the
algorithm.
x5: (-3,-2)
24=(√2,1)
22: (-1,0)
xy: (2,0)
(0,-1)
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