NPTEL Data Mining Week 5 Assignment Answers 2025

NPTEL Data Mining Week 5 Assignment Answers 2025

1. Support vector machine may be termed as:

A. Maximum aprori classifier
B. Maximum margin classifier
C. Minimum apriori classifier
D. Minimum margin classifier

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2. In a hard margin support vector machine:

A. No training instances lie inside the margin
B. All the training instances lie inside the margin
C. Only few training instances lie inside the margin
D. None of the above

Answer :- 

3. If the hyperplaneW^TX+b=0 correctly classifies all the training points (Xi, yi), where yi={+1, -1}, then:

A. ||W-1|| = 2
B. X= 1
C. W^TX_i+b ≥ 0 for all i
D. y_i(W^TX_i+b) ≥ 0 for all i

Answer :- 

4. The constraint in the primal optimization problem solved to obtain the hard margin optimal separating hyperplane is:

A. y_i(W^TX_i+b) ≥ 1 for all i
B. y_i(W^TX_i+b) ≤ 1 for all i
C. (W^TX_i+b) ≥ 1 for all i
D. (W^TX_i+b) ≤ 1 for all i

Answer :- 

5. The constraint in the dual optimization problem solved to obtain the hard margin optimal separating hyperplane is:

A. y_i(W^TX_i+b) ≥ 1 for all i
B. y_i(W^TX_i+b) ≤ 1 for all i
C. α_i ≥ 0, for all I, αiare the Lagrange multipliers
D. α_i ≤ 0, for all I, αiare the Lagrange multipliers

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6. In a hard margin SVM, support vectors lie –

A. inside the margin
B. on the margin
C. outside the margin
D. can be either inside or outside the margin

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7. Hessian matrix considered in SVM design has elements of the form:

A. X_i.X_j
B. yi – yj
C. y_iy_j(X_i– X_j)^T((X_i– X_j)
D. y_iy_jX_i.X_j

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8. The dual optimization problem in SVM design is usually solved using:

A. Genetic programming
B. Neural programming
C. Dynamic programming
D. Quadratic programming

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9. The generalization constant C is used to tune the:

A. test error only
B. training error only
C. relative weightage to training and test error
D. none of the above

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10. In a hard margin SVM W^TX+b=0, supposeX_j’s are the support vectors and α_j’s the corresponding Lagrange multipliers, then which of the following statements are correct:

A. W = S α_jy_jX_j
B. S α_jy_j= 0
C. Either A or B
D. Both A and B

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