Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Empirical margin distributions and bounding the generalization error of
  combined classifiers   We prove new probabilistic upper bounds on generalization error of complex
classifiers that are combinations of simple classifiers. Such combinations
could be implemented by neural networks or by voting methods of combining the
classifiers, such as boosting and bagging. The bounds are in terms of the
empirical distribution of the margin of the combined classifier. They are based
on the methods of the theory of Gaussian and empirical processes (comparison
inequalities, symmetrization method, concentration inequalities) and they
improve previous results of Bartlett (1998) on bounding the generalization
error of neural networks in terms of l_1-norms of the weights of neurons and of
Schapire, Freund, Bartlett and Lee (1998) on bounding the generalization error
of boosting. We also obtain rates of convergence in Levy distance of empirical
margin distribution to the true margin distribution uniformly over the classes
of classifiers and prove the optimality of these rates.

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The categories of this paper are: hep-ph
4
The categories of this paper are: cond-mat
19
The categories of this paper are: eess
16
The categories of this paper are: stat
9
The categories of this paper are: nlin
28
The categories of this paper are: alg-geom
13
The categories of this paper are: quant-ph
14
The categories of this paper are: math-ph
5
The categories of this paper are: gr-qc
26
The categories of this paper are: bayes-an
34
The categories of this paper are: chem-ph
37
The categories of this paper are: supr-con
11
The categories of this paper are: hep-lat
20
The categories of this paper are: acc-phys
21
The categories of this paper are: adap-org
22
The categories of this paper are: chao-dyn