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

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

3. Regularized Error Function In linear regression, we minimize the 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 Subject to: and Minimize:

6. Lagrangian Minimize:

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

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