Machine Learning - Deep Learning

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. ...

UNIT 3 Support Vector Machines MCQs  ----------------------------------------------------------------------------------------------------------------------------- 1. A Support Vector Machine can be used for A.     Performing linear or nonlinear classification B.     Performing regression C.     For outlier detection D.     All of the above Ans: D 2. The decision boundaries in a Support Vector machine is fully determined (or “supported”) by the instances located on the edge of the street? Top of Form True False Ans: A 3. Support Vector Machines are not sensitive to feature scaling A.     Top of Form True False Ans: B 4. If we strictly impose that all instances be off the street and on the right side, this is called  Soft margin classification  Hard margin classification  Strict margin classification  Loose margin classification Ans...

                                                                                              Unit III - A Support Vector Machines 1.       Linear SVM Classification 2.       Soft Margin Classification 3.       Nonlinear SVM Classification 4.       Polynomial Kernel 5.       Adding Similarity Features 6.       Gaussian RBF Kernel 7.       Computational Complexity

                                                                                                   Unit III - A Support Vector Machines 1.      Linear SVM Classification 2.      Soft Margin Classification 3.      Nonlinear SVM Classification 4.      Polynomial Kernel 5.      Adding Similarity Features 6.      Gaussian RBF Kernel 7.      Computational Complexity