Consider the following training dataset for detecting spam emails. +1 means the email is spam, and -1 means it is not. Apply the Winnow algorithm with threshold θ = 5. Assume the raising factor is 2 and the lowering factor is ½. First, find weight vector w. Visit each of the training emails once (stopping criteria), in the sequence of a, b, c, d, e, and f. Show all the intermediate steps. Second, determine if the new email (1, 1, 1, 0, 0) will be categorized as spam or non-spam using the weight vector w that you found in the first step and the given threshold θ (θ = 5). Show how you determine the answer.

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Author:James Kurose, Keith Ross
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Consider the following training dataset for detecting spam emails. +1 means the
email is spam, and -1 means it is not.
Apply the Winnow algorithm with threshold θ = 5.
Assume the raising factor is 2 and the lowering factor is ½.
First, find weight vector w. Visit each of the training emails once (stopping criteria), in the
sequence of a, b, c, d, e, and f. Show all the intermediate steps.
Second, determine if the new email (1, 1, 1, 0, 0) will be categorized as spam or non-spam
using the weight vector w that you found in the first step and the given threshold θ (θ = 5).
Show how you determine the answer.

#Email
a
b
с
d
e
f
and
0
1
0
1
0
0
orange
1
0
1
0
0
1
cheese
1
1
0
1
0
0
apple
0
1
1
0
1
0
vacation
0
1
0
0
1
0
y
-1
+1
+1
-1
+1
-1
Transcribed Image Text:#Email a b с d e f and 0 1 0 1 0 0 orange 1 0 1 0 0 1 cheese 1 1 0 1 0 0 apple 0 1 1 0 1 0 vacation 0 1 0 0 1 0 y -1 +1 +1 -1 +1 -1
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