Machine Learning 1: UNIT 3 A & B : Support Vector Machines Questions

UNIT – 3 A

SVM

Long Answer Questions

Bottom of Form

 

1.     How Soft Margin Classification works in SVM? Explain.

2.     What is the purpose of Nonlinear SVM Classification? Explain with example.

3.     Define SVM.  Explain Linear SVM Classification

4.     How can you classify the Nonlinear data by using adding Similarity Features? Explain.

5.     Explain Gaussian RBF Kernel in SVM.

6.     List the different models Computational Complexities.

7.     Explain the role of Polynomial Kernel in SVM.

UNIT 3 B

SVM

 

1.     SVM Regression

2.     Under the Hood

3.     Decision function and Predictions

4.     Training Objective

5.     Quadratic Programming

6.     The Dual problem

7.     Kernelized SVM

8.      Online SVMs

 

UNIT 3 B

Long Answer Qustions

 

1.     How should you set the Quadratic Programming parameters (H, f, A, and b) to solve the soft margin linear SVM classifier problem using an off-the-shelf QP solver?

2.     Explain dual form of the linear SVM.

3.     Explain Kernelized SVM

4.     How can you implement an online SVM classifier? Explain.

5.     Discuss training objective function of Hard Margin and Soft margin linear SVM classifier.

6.     Give the Decision function and Predictions of Linear SVM Classifier.

7.     Explain SVM Regression.