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질문 1 의 8

For linearly separable binary data, what geometric quantity does a hard-margin SVM seek to maximize?

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  1. For linearly separable binary data, what geometric quantity does a hard-margin SVM seek to maximize?
    • The number of deleted training rows
    • The image brightness of every input
    • The separating margin
    • The number of hidden network layers
  2. Which training examples contribute as support vectors in a kernel soft-margin SVC decision function?
    • Only correctly classified points nearest the boundary in every case
    • Examples with nonzero dual coefficients, including possible margin violators
    • Every training example with exactly equal weight by definition
    • Only the class label strings without their feature values
  3. What computation does a kernel function replace in the SVM formulation?
    • All training labels
    • The requirement for any input examples
    • Every numerical comparison with an alphabetical sort
    • Inner products in an implicit feature representation
  4. What does C weight in a standard soft-margin SVM objective?
    • The penalty for margin violations relative to regularization
    • The mandatory number of output colors
    • The file extension of every training example
    • The number of classes in every possible task
  5. 유연한 곡선 경계를 만드는 데 일반적으로 사용되는 커널은 무엇입니까?
    • JPEG 커널
    • 방사형 기저 함수(RBF) 커널
    • TCP 커널
    • 알파벳 커널
  6. Which setting makes an SVM a candidate worth evaluating, without guaranteeing its performance?
    • Only inputs with no feature representation
    • A rule that billions of examples are always inexpensive to fit
    • Many input features relative to the number of examples
    • Only audio files with no numerical encoding
  7. With a fixed RBF input distance, what does a larger positive gamma do to similarity decay as distance increases?
    • Make it decay more rapidly, producing more localized influence
    • Make it decay more slowly, producing a broader range of influence
    • Make the similarity equal for all nonzero distances
    • Set the penalty for margin violations while leaving similarity unchanged
  8. Why can changing feature units affect an RBF SVM unless preprocessing addresses the scales?
    • RBF compares category order while ignoring coordinate distances
    • A constant C automatically cancels every change of feature units
    • A learned intercept always standardizes every input coordinate
    • The kernel uses distances that depend on feature magnitudes