Consider the dataset below that has 4 instances, each with two features. Note that there is no bias or constant feature and you should not add one. Given that the weight vector is initialized to be all zeroes, what is the weight vector after the perceptron algorithm has made one complete pass over the data in the order shown? X1 -2 X2 Y -2 1 -2 2 1 -1 1 -1 2 2 1 Will the perceptron converge on this dataset? Why or why not?
Consider the dataset below that has 4 instances, each with two features. Note that there is no bias or constant feature and you should not add one. Given that the weight vector is initialized to be all zeroes, what is the weight vector after the perceptron algorithm has made one complete pass over the data in the order shown? X1 -2 X2 Y -2 1 -2 2 1 -1 1 -1 2 2 1 Will the perceptron converge on this dataset? Why or why not?
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
Transcribed Image Text:Consider the dataset below that has 4 instances, each with two features. Note
that there is no bias or constant feature and you should not add one. Given that the weight
vector is initialized to be all zeroes, what is the weight vector after the perceptron algorithm
has made one complete pass over the data in the order shown?
X1 X2 Y
-2 -2 1
DI
-2
2
-1
-1
2
1
212
11
Will the perceptron converge on this dataset? Why or why not?
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