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Machine Learning 101 Practice Test: Support Vector Machines
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Quiz on support vector machines (SVMs), covering key concepts like the large margin intuition, margins and hard/soft SVMs, norm regularization, optimality conditions and support vectors, and finally, implementing soft SVMs using Stochastic Gradient Descent (SGD). A support vector machine (SVM) is a supervised machine learning algorithm that can generalize between two classes. SVMs are used for classification and regression tasks, and are particularly good at solving binary classification problems.  Here are some details about SVMs: Objective: Find a hyperplane with the highest margin,... Show more
Machine Learning 101 Practice Test: Support Vector Machines
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25 Questions

1. Which of the following statements is not true about the role of C in SVM?
2. Suppose the optimal separating hyperplane is given by 2x1 + 4x2 + x3 − 4 = 0 and the class labels are +1 and -1. For the training example (1, 0.5, 1), the class label is -1, and is a support vector.
3. Minimizing a quadratic objective function (w\(_i^2\)) subject to certain constraints where i= 1 to n, in SVM is known as primal formulation of linear SVMs.
4. Which of the following statements is not true about SVM?
5. The bounds derived for Soft-SVM do not depend on the dimension of the instance space.
6. Support vector machine is a generative classifier.
7. Gradient descent and Lagrange are interchangeably used by SVM.
8. The optimal classifier is the one with the largest margin.
9. Which of the following statements is not true about support vectors?
10. Which of the following statements is not true about hyperplane in SVM?
11. The output of hard-SVM is the separating hyperplane with the largest margin.
12. Given the Soft SVM optimization problem and the update rule of SGD w(t+1) = – (\(\frac {1}{\lambda t}\) ∑\(^t _{j=1}\)vj). For the hinge loss, given an example (x, y), it can choose Vj to be one if y (w(J), x) ≥ 1.
13. Which of the following statements is not true about large margin intuition classifier?
14. Karush–Kuhn–Tucker (KKT) conditions are second derivative tests for a solution in nonlinear programming to be optimal, provided that some regularity conditions are satisfied.
15. Support vector machines cannot be used for regression.
16. The goal of a support vector machine is to find the optimal separating hyperplane which minimizes the margin of the training data.
17. Which of the following statements is not true about margin in SVM?
18. Which of the following statements is not true about SVM?
19. Let the optimization problem using Soft SVM is minimize a function and the update rule of SGD is w(t+1) = – (\(\frac {1}{\lambda t}\) ∑\(^t _{j=1}\)vj) then Vj is the sub gradient of the loss function.
20. The difference between the primal and dual solutions is known as duality gap.
21. Which of the following statements is not true about maximum margin?
22. Support Vector Machines are known as Large Margin Classifiers.
23. The maximum margin linear classifier is the linear classifier with the maximum margin.
24. The slack variables value ξi ≥ 1 for misclassified points, and 0 < ξi < 1 for points close to the decision boundary.
25. Suppose we have an equality optimization problem as follows: Minimize f(x, y) = x + y subject to x2 + y2 – 2 = 0. While solving the above equation what will be the value x, y and λ?

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