Training
• Probabilistic
Approximation
–Filling the histogram bins of Parts
•AdaBoost :
–Train Multiple Classifiers ht(x) with weighted training samples.
–First Classifier h1(x) – equal weights to all.
–Next – Higher weight to Incorrectly classified
samples
–Final Classifier:
–αt found by binary search
–The weighted
sum of classifiers is reduced to a single classifier due
to linearity (in log likelihood).
–Use Cross
Validation to prevent overfitting
