## Latest Support Vector Machine MCQ’s with answers

Question 1 :

Contents

SVM stands for?

Options :

a. Simple Vector Machine

b. Support Vector Machine

c. Super Vector Machine

d. All the Above

`Answers : b. Support Vector Machine`

Question 2 :

SVM is classified into how many types?

Options :

a. One

b. Two

c. Three

d. Four

`Answers : b. Two`

Question 3 :

SVM, which best segregates classes into how many classes?

Options :

a. One

b. Two

c. Three

d. Four

`Answers : b. Two`

Question 4 :

SVM is a supervised Machine Learning can be used for

Options :

a. Regression

b. Classification

c. Either a or b

d. None of These

`Answers : c. Either a or b`

Question 5 :

Linear separator, Hyper plane

Options :

a. f(x)=sign(w/x+b)

b. f(x)=sign(w+x+b)

c. f{x)=sign(w.x+b)

d. f(x)=sign(w-x+b)

`Answers : c. f{x)=sign(w.x+b)`

Question 6 :

In Hyper plane, f(x)=sign(w*x+b) where ‘w’ is a?

Options :

a. Constant

b. Vector

c. Distance

d. None of the Above

`Answers : b. Vector`

Question 7 :

Closest Point to the hyper plane are support vectors

Options :

a. True

b. False

c. Unpredictable

d. None of these

`Answers : a. True`

Question 8 :

Where p is ?

Options :

a. Constant

b. Null

c. Margin

d. Hyper plane

`Answers : c. Margin`

Question 9 :

By maximizing the distances between nearest data point and
hyper plane will help us to decide the right hyper-plane.

Options :

a. Margin

b. Mercer’s Theorem

c. Regression

d. None of these

`Answers : a. Margin`

Question 10 :

Slack variables ε, can be added to allow misclassification of
difficult or noisy examples, resulting margin is called?

Options :

a. Soft Margin

b. Null Margin

c. High Margin

d. Low Margin

`Answers :a. Soft Margin `

Question 11 :

Every semi-positive definite symmetric function is a kernel

Options :

a. Mercer’s theorem

b. Bayes Theorem

c. Probabilistic Theorem

d. None of the Above

`Answers :a. Mercer's theorem`