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Which of the following describes a support vector machine (SVM)?

  1. AIt draws the boundary so that the average distance from all points to the boundary is maximized, using all the training points as evenly as possible
  2. BAmong the boundaries that separate two classes, it chooses the one that maximizes the distance (margin) to the data closest to the boundary (the support vectors)
  3. CIt assumes that each feature is mutually independent within each class and classifies by computing the probability of each class with Bayes' theorem
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Answer: B. Among the boundaries that separate two classes, it chooses the one that maximizes the distance (margin) to the data closest to the boundary (the support vectors)

An SVM is a classification method that chooses, among the boundaries (hyperplanes) separating two classes, the one with the largest margin. The boundary is determined only by the few data points closest to it (the support vectors) and does not change if other data points move slightly. A boundary with a large margin is considered less likely to make mistakes on new data as well. For data that cannot be separated by a straight line, the kernel method gives the same effect as separating the data in a higher-dimensional space.

  • A. Only the support vectors closest to the boundary determine the boundary.
  • B. Correct. It chooses the boundary that maximizes the margin.
  • C. This describes naive Bayes classification.
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