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SVM for Regression

SVM for Regression. DMML Lab 04/20/07. SVM Recall. Two-class classification problem using linear model:. In linear regression, we minimize the error function:. Regularized Error Function. Replace the quadratic error function by Є -insensitive error function :.

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SVM for Regression

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  1. SVM for Regression DMML Lab 04/20/07

  2. SVM Recall Two-class classification problem using linear model:

  3. In linear regression, we minimize the error function: Regularized Error Function Replace the quadratic error function by Є-insensitive error function: An example of Є-insensitive error function:

  4. Slack Variables For a target point to lie inside the tube: Introduce slack variables to allow points to lie outside the tube:

  5. Error Function for Support Vector Regression Minimize: Subject to: and

  6. Lagrangian Minimize:

  7. Dual Form of Lagrangian Maximize: Prediction can be made using:

  8. How to determine b? Karush-Kuhn-Tucker (KKT) conditions: Support vectors are points that lie on the boundary or outside the tube

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