Suppose we train a hard-margin linear SVM on n > 100 datapoints in R₂, yielding a hyperplane with exactly 2 support vectors.

Question

3 years ago

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Suppose we train a hard-margin linear SVM on n > 100 datapoints in R₂, yielding a hyperplane with exactly 2 support vectors.

If we add one more datapoint 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)? Select one of: {2, 3, n, n + 1}. Optional: draw a case that justifies your answer

Mathematics

1 answer:

svlad2 [7] · Answer 1 · 2022-03-28T03:48:45+03:00

svlad2 [7]3 years ago

5 0

Answer:It is option one

Step-by-step explanation:It jusyt is