Question 1 :
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
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