Material covered
- Balancing data fit and model complexity with Ridge regression
- Alternative way to write optimization as finding parameters within disk
- Behavior of model weights as disk radius is changed (demo)
- "Evidence-sharing" and geometric reasons for non-sparse solutions
- Lasso regression: replacing L2 model penalty with L1 model penalty
- Casting Lasso regression as a scilab/matlab quadratic program
- Behavior of model weights as L1 limit is changed (demo)
- Reasons for Lasso giving more sparse solutions
- Implicit feature selection effect
- Handling non-linear regression by lifting data points to higher dimension
- Classification with small discrete label sets
- Why classification loss is fundamentally different from regression
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Last modified: Saturday, 25 August 2007, 10:15 AM