•Training (step t)
•Sampling
Distribution
• - a probability that example i from the
original training dataset is selected
• for the first step
(t=1)
•Take
K samples from the training set according to
•Train
a classifier ht on the
samples
•Calculate
the error of ht :
•Classifier
weight:
•New
sampling distribution