Posted by Gunnar Raetsch on December 30, 19102 at 12:28:39:
I would like to announce a new paper on large margins and Boosting. I've added it to the publications list.
Please find the abstract below.
Cheers, Gunnar
Abstract:
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.