SVM

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?

• Yes

• No

If you remove the non-red circled points from the data, the decision boundary will change?

• True

• False

When the C parameter is set to infinite, which of the following holds true?

• The optimal hyperplane if exists, will be the one that completely separates the data

• The soft-margin classifier will separate the data

• None of the above

The effectiveness of an SVM depends upon:

• Selection of Kernel

• Kernel Parameters

• Soft Margin Parameter C

• All of the above

The SVM’s are less effective when:

• The data is linearly separable

• The data is clean and ready to use

• The data is noisy and contains overlapping points

What would happen when you use very small C (C~0)?

• Misclassification would happen

• Data will be correctly classified

• Can’t say

• None of these

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

• Text and Hypertext Categorization

• Image Classification

• Clustering of News Articles

• All of the above