Answer-first summary for fast verification
Answer: The algorithm determines the boundary that is the furthest away from all the observations.
## Explanation **Option B is correct** because support vector machines (SVMs) aim to find the optimal hyperplane that maximizes the margin between classes. The support vectors are the data points closest to the decision boundary, and the algorithm seeks to maximize the distance between these support vectors and the decision boundary. **Option A is incorrect** because adding training data away from the support vectors typically does not affect the decision boundary, as the boundary is determined primarily by the support vectors themselves. **Option C is incorrect** because in soft margin classification, the algorithm balances margin width and classification errors using a regularization parameter (C). It doesn't minimize total error penalty regardless of margin width; rather, it finds a trade-off between maximizing margin and minimizing classification errors.
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Which of the following statements about the support vector machine algorithm is most accurate?
A
Adding more training data away from the support vectors will affect the boundary.
B
The algorithm determines the boundary that is the furthest away from all the observations.
C
In soft margin classification, the discriminant boundary chosen minimizes the total error penalty regardless of margin width.
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