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Suppose we train a hard-margin linear SVM on n 100 data...

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Machine Learning
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Suppose we train a hard-margin linear SVM on n > 100 data points in R2, yielding a hyperplane with exactly 2 support vectors. If we add one more data point and retrain the classifier, what is the maximum possible number of support vectors for the new hyperplane (assuming the n + 1 points are linearly separable)?

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Correct Answer: Option D
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