Bounding the generalization error of convex combinations of classifiers: balancing the dimensionality and the margins

Details Bounding the generalization error of convex combinations of classifiers: balancing the dimensionality and the margins Bounding the generalization error of convex combinations of classifiers: balancing the dimensionality and the margins

2004-05-18

arxiv.org/abs/math/0405345v1

2003 Ann. Appl. Probab. 13 No. 1

Mathematics

Probability

35 pages, 7 figures

Scientific paper

A problem of bounding the generalization error of a classifier f in H, where H is a "base" class of functions (classifiers), is considered. This problem frequently occurs in computer learning, where efficient algorithms of combining simple classifiers into a complex one (such as boosting and bagging) have attracted a lot of attention. Using Talagrand's concentration inequalities for empirical processes, we obtain new sharper bounds on the generalization error of combined classifiers that take into account both the empirical distribution of "classification margins'' and an "approximate dimension" of the classifiers and study the performance of these bounds in several experiments with learning algorithms.

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