Maximum Margin Classifiers in Support Vector Machines

Choose a study mode

Play Quiz
Study Flashcards
Spaced Repetition
Chat to Lesson

Podcast

Play an AI-generated podcast conversation about this lesson
Download our mobile app to listen on the go
Get App

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 (C)</p> Signup and view all the answers

What does the hyperplane H0 represent in SVM?

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

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

<p>Allowing misclassification (A)</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 (A)</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 (C)</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 (C)</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 (A)</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 (D)</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 (D)</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-) (A)</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 (C)</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 (B)</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 (D)</p> Signup and view all the answers

What is the characteristic of the 𝑊 vector in SVM?

<p>Orthogonal to H1 line (A)</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 (C)</p> Signup and view all the answers

Flashcards are hidden until you start studying

More Like This

Use Quizgecko on...
Browser
Browser