1) Consider the data set given below. x1 X2 Y 0 0 M 0 1 1 0 1 1 1 0 Claim: PLA (perceptron learning algorithm) can learn a classifier that achieves zero misclassification error on the training data. This claim is: O True O False O Depends on the initial weights O True, only if we normalize the feature vectors before applying PLA.

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1) Consider the data set given below.
x1 x2 Y
0 0
M
0 1
1 0 1
1
1
0
Claim: PLA (perceptron learning algorithm) can learn a classifier that achieves zero misclassification error on the training data. This claim is:
O True
O False
O Depends on the initial weights
O True, only if we normalize the feature vectors before applying PLA.
Transcribed Image Text:1) Consider the data set given below. x1 x2 Y 0 0 M 0 1 1 0 1 1 1 0 Claim: PLA (perceptron learning algorithm) can learn a classifier that achieves zero misclassification error on the training data. This claim is: O True O False O Depends on the initial weights O True, only if we normalize the feature vectors before applying PLA.
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