Speaker: Chien-I Liao
Title: Efficient Margin Maximizing with Boosting
Date: December 13th, 2005.

AdaBoost produces a linear combination of base hypotheses and predicts
with the sign of this linear combination. It has been observed that the
generalization error of the algorithm continues to improve even after
all examples are classified correctly by the current signed linear
combination, which can be viewed as hyperplane in feature space where
the base hypotheses form the features. The improvement is attributed to
the experimental observation that the distances (margins) of the
examples to the separating hyperplane are increasing even when the
training error is already zero; that is, all examples are on the
correct side of the hyperplane.

We give a new version of AdaBoost, called AdaBoost*, that explicitly
maximizes the minimum margin of the examples up to a given precision.
The algorithm incorporates a current estimate of the achievable margin
into its calculation of the linear coeffecients of the base hypotheses.
The number of base hypotheses needed is essentially the same as the
number needed by a previous AdaBoost related algorithm that required an
explicit estimate of the achievable margin.

Reference: http://www.boosting.org/papers/RaeWar03.pdf
Slides available at: http://cs.nyu.edu/~cil217/ML/ml.htm