1. A simple classifier Ridge regression A variation on standard linear regression Adds a “ridge” term that has the effect of “smoothing” the weights Equivalent to training a linear network with weight decay.

2. A “Strong” Classifier: SNoW– Sparse Network of Winnows Roth et al. 2000 – Currently best reported face detector 1. Turn each pixel into a sparse, binary vector 2. Activation = sign( ) 3. Train with the Winnow update rule

3. AdaBoost for Feature Selection Viola and Jones (2001) used AdaBoost as a feature selection method For each round of AdaBoost: For each patch, train a classifier using only that one patch. Select the best one as the classifier for this round reweight distribution based on that classifier.

4. Results

5. AdaBoost consistently improves performance