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Prueba de máquinas de vectores de soporte

Revise la separación de márgenes estrictos, los vectores de soporte de márgenes flexibles, los núcleos, C, la similitud de RBF, el uso de alta dimensión, la gamma y el escalado de características.

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Pregunta 1 de 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. ¿Qué núcleo se utiliza habitualmente para crear límites curvos y flexibles?
    • El núcleo JPEG
    • El núcleo de la función de base radial (RBF)
    • El núcleo TCP
    • El núcleo del alfabeto
  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