NPTEL Data Mining Assignment Answers 2023 -Week 5

Are you a student struggling with the Data Mining NPTEL Week 5 assignment? Look no further! In this article, we have compiled a set of hints and answers to help guide you through the assignment.

Make sure to give it a try on your own first, but use these hints as a helpful resource.

NPTEL DATA MINING ASSIGNMENT ANSWERS 2023 -WEEK 5

Q1. Support vector machine is:

Answer: (B)Maximum margin classifier

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Q2. Support vectors in SVM are:

Answer: (C) Subset of training data points

Q3. In a hard margin support vector machine:

Answer: (B) All the instances lie inside the margin

Q4. The Lagrange multipliers corresponding to the support vectors have a value:

Answer: (C) greater than zero

Q5. The primal optimization problem solved to obtain the hard margin optimal separating hyperplane is:

Answer: (A) Minimize N * W ^ T * W such that y{i}(W ^ T * x{i} + b) >= 1 for all i

Q6. The dual optimization problem solved to obtain the hard margin optimal separating hyperplane is:

Answer: (D) Maximize 1/2 * W ^ T * W + Sigma*alpha_{i} such that y{i}(W ^ T * X + b) <= 1 for all i

Q7. We are designing a SVM, W^Tx+b=0, suppose X/s are the support vectors and alpha / s the corresponding Lagrange multipliers, then which of the following statements are correct:

Answer: (D) Both A and B

Q8. If the hyperplane W^Tx + b = 0 correctly classifies all the training points (Xi, Yi) where Yi = {+1, – 1} then:

Answer: (C) W^TX+b >=0 for all i

Q9. The dual optimization problem in SVM design is usually solved using:

Answer: (D) Quadratic programming

Q10. Slack variables are used in which of the below:

Answer: (A) Soft margin SVM

Disclaimer: Please keep in mind that these answers are intended to serve as a reference for students. Our website does not guarantee the accuracy of the answers provided. We encourage all students to complete their assignments independently and use these answers as a supplement to their own understanding.

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