Title: Quiz Ch 5
Course: Machine Learning Security - Winter WC > Section: Unit 5

Q: Which statement is NOT true for Support Vector Machines?
- SVMs work well with large datasets
- SVMs perform large margin classification
- SVMs are sensitive to feature scale
- SVMs work well with small datasets
A: SVMs work well with large datasets

Q: Which SVM type can be greatly disrupted by a single outlier?
- Hard margin classification
- Large margin classification
- Soft margin classification
- None of these
A: Hard margin classification

Q: If your SVM is overfitting, which action will improve it?
- Reduce C
- Increase C
- Add more data
- Use hard margins
A: Reduce C

Q: Which technique measures how much each instance resembles a landmark?
- Similarity features
- Regularization
- Polynomial kernel
- Hard margins
A: Similarity features

Q: Which technique gives results like using similarity features but with less compute time?
- Gaussian RBF kernel
- Polynomial features
- String kernel
- Regularization
A: Gaussian RBF kernel