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SVM Quiz

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Questions and Answers

What is the default behavior of SVM as a classifier?

It works as a linear classifier (correct)

It works as a clustering algorithm

It works as a dimensionality reduction technique

It works as a non-linear classifier

How is a data set defined as linearly separable in the context of SVM?

If it has a non-linear decision boundary

If it has a high dimensional feature space

If a hyperplane can separate the data into required classes (correct)

If it can only be separated by non-linear methods

What is the decision boundary in 3 dimensions for a linearly separable data set?

A line

A point

A plane (correct)

A hyperplane

In general, what is the separator for a linearly separable data set in d dimensions?

<p>(d-1) dimensional hyperplane</p> Signup and view all the answers

What are the primary applications of SVM?

<p>Classification</p> Signup and view all the answers

Study Notes

SVM Classifier Behavior

Defaults to a soft margin classifier, allowing for some misclassifications to achieve better overall performance

Linearly Separable Data Set

A data set is defined as linearly separable if it can be separated by a single hyperplane

Decision Boundary in 3 Dimensions

The decision boundary for a linearly separable data set in 3 dimensions is a plane that separates the classes

Separator in d Dimensions

In d dimensions, the separator for a linearly separable data set is a (d-1) dimensional hyperplane that separates the classes

Primary Applications of SVM

Classification and regression tasks, particularly in text classification, image classification, and bioinformatics
Handling high-dimensional data and noisy data
Outperforming traditional machine learning methods in many applications

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Description

Test your knowledge of Support Vector Machines (SVM) in machine intelligence with this quiz. Explore the concepts and applications of SVM under the guidance of Preet Kanwal, Associate Professor in the Department of Computer Science & Engineering.