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In SVM, what does the term 'support vectors' refer to?

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Question: In SVM, what does the term \'support vectors\' refer to?

Options:

  1. Data points that are farthest from the decision boundary
  2. Data points that lie on the decision boundary
  3. All data points in the dataset
  4. Data points that are misclassified

Correct Answer: Data points that lie on the decision boundary

Solution:

Support vectors are the data points that lie closest to the decision boundary and are critical in defining the position and orientation of the boundary.

In SVM, what does the term 'support vectors' refer to?

Practice Questions

Q1
In SVM, what does the term 'support vectors' refer to?
  1. Data points that are farthest from the decision boundary
  2. Data points that lie on the decision boundary
  3. All data points in the dataset
  4. Data points that are misclassified

Questions & Step-by-Step Solutions

In SVM, what does the term 'support vectors' refer to?
  • Step 1: Understand that SVM stands for Support Vector Machine, which is a type of machine learning algorithm used for classification tasks.
  • Step 2: In SVM, a decision boundary is a line (or hyperplane) that separates different classes of data points.
  • Step 3: Support vectors are specific data points that are closest to this decision boundary.
  • Step 4: These support vectors are important because they help determine where the decision boundary is placed.
  • Step 5: If you remove a support vector, the position of the decision boundary may change, but removing other points that are not support vectors will not affect it.
  • Support Vectors – Support vectors are the data points that are closest to the decision boundary in SVM and are essential for determining the boundary's position and orientation.
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