The Gaussian kernel is also called the RBF kernel, for Radial Basis Functions.

To build a modularized kernel, the kernel-headers and kernel-source packages must first be installed. Where will you find the kernel-headers and kernel-source?

A

/usr/local

B

/usr/src/linux

C

/usr/sre/redhat

D

/usr/bin

E

None of the above

We usually use feature normalization before using the Gaussian kernel in SVM. What is true about feature normalization?
1. We do feature normalization so that new feature will dominate other
2. Some times, feature normalization is not feasible in case of categorical variables
3. Feature normalization always helps when we use Gaussian kernel in SVM

A

1

B

1 and 2

C

1 and 3

D

2 and 3

Give the correct Answer for following statements. 1. It is important to perform feature normalization before using the Gaussian kernel. 2. The maximum value of the Gaussian kernel is 1.

A

1 is true, 2 is false

B

1 is false, 2 is true

C

1 is true, 2 is true

D

1 is false, 2 is false

Solving a non linear separation problem with a hard margin Kernelized SVM (Gaussian RBF Kernel) might lead to overfitting

A

TRUE

B

FALSE

As per the RBI Act, 1934, the following functions are described as the functions of a Central Bank:
1. Banking functions
2. Advisory functions
3. Supervisory functions
4. Promotional functions
Identify the correct combination:

A

1, 3 and 4

B

1, 2 and 4

C

2, 3 and 4

D

Only 1 and 3

In SVM, RBF kernel with appropriate parameters to perform binary classification where the data is non-linearly seperable. In this scenario

A

the decision boundry in the transformed feature space in non-linear

B

the decision boundry in the transformed feature space in linear

C

the decision boundry in the original feature space in not considered

D

the decision boundry in the original feature space in linear

Suppose you are using RBF kernel in SVM with high Gamma value. What does this signify?

A

The model would consider even far away points from hyperplane for modeling

B

The model would consider only the points close to the hyperplane for modeling

C

The model would not be affected by distance of points from hyperplane for modeling

D

None of the above

The Fourier transform of a Gaussian pulse is also a Gaussian pulse.

A

True

B

False

For radial equilibrium flow radial component must be _______ along the radial direction of flow.

A

higher

B

zero

C

lesser

D

none of the mentioned

Any linear combination of the components of a multivariate Gaussian is a univariate Gaussian.

A

TRUE

B

FALSE

The Gaussian kernel is also called the RBF kernel, for Radial Basis Functions.

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