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Support Vector Machines: Maximizing Margin for Linear and Nonlinear Classification

This lecture outline discusses support vector machines (SVM) and how to find a linear hyperplane (decision boundary) that separates data. It explores different possible solutions and defines criteria for determining which solution is better. It also covers SVM techniques for non-linearly separable problems by introducing slack variables and transforming data into higher dimensional space.

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Support Vector Machines: Maximizing Margin for Linear and Nonlinear Classification

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  1. Lecture outline • Support vector machines

  2. Find a linear hyperplane (decision boundary) that will separate the data Support Vector Machines

  3. One Possible Solution Support Vector Machines

  4. Another possible solution Support Vector Machines

  5. Other possible solutions Support Vector Machines

  6. Which one is better? B1 or B2? How do you define better? Support Vector Machines

  7. Find hyperplane maximizes the margin => B1 is better than B2 Support Vector Machines

  8. Support Vector Machines

  9. Support Vector Machines • We want to maximize: • Which is equivalent to minimizing: • But subjected to the following constraints: • This is a constrained optimization problem • Numerical approaches to solve it (e.g., quadratic programming)

  10. Support Vector Machines • What if the problem is not linearly separable?

  11. Support Vector Machines • What if the problem is not linearly separable? • Introduce slack variables • Need to minimize: • Subject to:

  12. Nonlinear Support Vector Machines • What if decision boundary is not linear?

  13. Nonlinear Support Vector Machines • Transform data into higher dimensional space

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