Support Vector Machine (SVM)

[True or False] If you remove the non-red circled points from the data, the decision boundary will change?

A) True B) False

• A

• B

What do you mean by generalization error in terms of the SVM?

A) How far the hyperplane is from the support vectors B) How accurately the SVM can predict outcomes for unseen data C) The threshold amount of error in an SVM

• A

• B

• C

The effectiveness of an SVM depends upon:

A) Selection of Kernel B) Kernel Parameters C) Soft Margin Parameter C D) All of the above

• A

• B

• C

• D

Support vectors are the data points that lie closest to the decision surface.

A) TRUE B) FALSE

• A

• B

Which of the following are real world applications of the SVM?

A) Text and Hypertext Categorization B) Image Classification C) Clustering of News Articles D) All of the above

• A

• B

• C

• D

Suppose you are using a Linear SVM classifier with 2 class classification problem. Now you have been given the following data in which some points are circled red that are representing support vectors.

If you remove the following any one red points from the data. Does the decision boundary will change?

A) Yes B) No

• A

• B