Quizzes on SVM (Support Vector Machines) and kernel tricks.

What is the objective of a Support Vector Machine?
A) To find a hyperplane that separates data points into different classes with maximum margin.
B) To minimize the error rate of classification.
C) To find the nearest neighbors of a data point.
D) To perform clustering of data points.

In an SVM, what are support vectors?
A) The data points farthest from the hyperplane.
B) The data points that are closest to the hyperplane.
C) The data points that are used to calculate the margin.
D) Both B and C.

What is the margin in an SVM?
A) The distance between the two support vectors.
B) The distance between a data point and the hyperplane.
C) The distance between the hyperplane and the nearest support vector.
D) The distance between the hyperplane and the farthest support vector.

What is the purpose of the regularization parameter (C) in SVM?
A) To control the size of the margin.
B) To control the trade-off between maximizing the margin and minimizing classification error.
C) To control the kernel type used.
D) To control the number of support vectors.

In SVM, what does the kernel function ( K(x, x’) ) compute?
A) The distance between ( x ) and ( x’ ).
B) The dot product of ( x ) and ( x’ ) in the original space.
C) The dot product of ( x ) and ( x’ ) in the transformed feature space.
D) The similarity between ( x ) and ( x’ ) in the original space.

In the context of SVMs, what is a slack variable?
A) A variable that allows some data points to be misclassified.
B) A variable that defines the margin.
C) A variable that determines the type of kernel used.
D) A variable that adjusts the hyperplane position.