Maximum Margin Classifiers in Support Vector Machines
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Questions and Answers

What happens to the margin position of the hyperplane if the support vectors are deleted?

  • The margin position increases
  • The margin position shifts towards the support vectors
  • The margin position decreases
  • The margin position remains unchanged (correct)

    • In a linearly separable dataset, why are not all points on the supporting hyperplanes considered support vectors?

  • Because they do not influence the classifier's decision boundary (correct)
  • Because they are not important for maximizing the margin
  • Because they are outliers
  • Because they are misclassified points

    • What is the main goal when searching for the largest margin classifier in SVM?

  • To minimize the number of support vectors
  • To maximize the distance between the classes (correct)
  • To minimize the margin width
  • To maximize the number of support vectors

    • How are the weights in SVM determined during optimization?

    <p>Only the support vectors determine the weights</p> Signup and view all the answers

    What does the hyperplane H0 represent in SVM?

    <p>The median plane between H1 and H2</p> Signup and view all the answers

    What is the key difference between Maximum Margin Classifiers and Support Vector Classifiers?

    <p>Allowing misclassification</p> Signup and view all the answers

    Why are support vectors crucial in determining the decision boundary in SVM?

    <p>They define the boundaries of each class</p> Signup and view all the answers

    Why are Maximum Margin Classifiers very sensitive to outliers?

    <p>They optimize the margin based on all data points</p> Signup and view all the answers

    What is the purpose of allowing some misclassification in Support Vector Classifiers?

    <p>To improve the generalization ability</p> Signup and view all the answers

    How does a hard margin differ from a soft margin in Support Vector Machines?

    <p>Soft margin allows some errors for better generalization</p> Signup and view all the answers

    What trade-off is involved when using a soft margin in Support Vector Machines?

    <p>Reduced sensitivity to outliers at the expense of model complexity</p> Signup and view all the answers

    How is the best soft margin determined in Support Vector Machines?

    <p>Through cross-validation to find the best trade-off between errors and margin size</p> Signup and view all the answers

    What is the formula for the margin of a separating hyperplane in Support Vector Machines?

    <p>(d+) + (d-)</p> Signup and view all the answers

    Why should the decision boundary in SVM be as far away from the data of both classes as possible?

    <p>To maximize the margin</p> Signup and view all the answers

    In SVM, how can a data point be identified as a support vector?

    <p>By removing it resulting in increasing the margin</p> Signup and view all the answers

    What is the equation that corresponds to the decision boundary in SVM when trained on a dataset with 6 points, containing three samples with class label -1 and three samples with class label +1?

    <p>𝑊𝑋 + b = 0</p> Signup and view all the answers

    What is the characteristic of the 𝑊 vector in SVM?

    <p>Orthogonal to H1 line</p> Signup and view all the answers

    Why are methods like Lagrange multiplier method used in practice to solve SVM?

    <p>To handle non-linearly separable data</p> Signup and view all the answers

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