We are given the following training examples in 2D ((-3,5), +), ((-4,-2), +), ((2,1),-), ((4,3),-) Use +1 to map positive (+) examples and -1 to map negative (-) examples. We want to apply the learning algorithm for training a perceptron using the above data with starting weights wo = W₁ = W₂ = 0 and learning rate n = 0.1. In each iteration process the training examples in the order given above. Complete at most 3 iterations over the above training examples. What are the weights at the end of each iteration? Are these weights final?

Advanced Engineering Mathematics
10th Edition
ISBN:9780470458365
Author:Erwin Kreyszig
Publisher:Erwin Kreyszig
Chapter2: Second-order Linear Odes
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We are given the following training examples in 2D
((-3,5), +), ((-4, –2), +), ((2,1), -), ((4,3),–)
Use +1 to map positive (+) examples and -1 to map negative (-) examples.
We want to apply the learning algorithm for training a perceptron using the above data with
starting weights wo = w1 = w2 = 0 and learning rate 7 = 0.1. In each iteration process the training
examples in the order given above. Complete at most 3 iterations over the above training examples.
What are the weights at the end of each iteration?
Are these weights final?
Transcribed Image Text:We are given the following training examples in 2D ((-3,5), +), ((-4, –2), +), ((2,1), -), ((4,3),–) Use +1 to map positive (+) examples and -1 to map negative (-) examples. We want to apply the learning algorithm for training a perceptron using the above data with starting weights wo = w1 = w2 = 0 and learning rate 7 = 0.1. In each iteration process the training examples in the order given above. Complete at most 3 iterations over the above training examples. What are the weights at the end of each iteration? Are these weights final?
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