SVM for Regression DMML Lab 04/20/07. SVM Recall Two-class classification problem using linear model:

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Presentation transcript:

SVM for Regression DMML Lab 04/20/07

SVM Recall Two-class classification problem using linear model:

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:

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

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

Lagrangian Minimize:

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

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