a simple classifier ridge regression a variation on standard linear regression adds a “ridge”...
Post on 20-Dec-2015
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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.
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
wixi∑
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.
Results
.00%
.20%
.40%
.60%
.80%
Single SNoWSNoW + BaggingRidge + AdaBoostSNoW + AdaBoost
SNoW + Bagging + patchesSNoW + AdaBoost + patchesRidge+AdaBoost+ patches
East
AdaBoost consistently improves performance
0 %
5 %
10 %
15 %
20 %
25 %
Global +Ridge
Global +SNoW
Patches +Ridge
Patches +SNoW
Single SystemBaggingAdaBoost