Kavli Affiliate: Wei Gao
| First 5 Authors: Meng-Zhang Qian, Zheng Ai, Teng Zhang, Wei Gao,
| Summary:
Margin has played an important role on the design and analysis of learning
algorithms during the past years, mostly working with the maximization of the
minimum margin. Recent years have witnessed the increasing empirical studies on
the optimization of margin distribution according to different statistics such
as medium margin, average margin, margin variance, etc., whereas there is a
relative paucity of theoretical understanding. In this work, we take one step
on this direction by providing a new generalization error bound, which is
heavily relevant to margin distribution by incorporating ingredients such as
average margin and semi-variance, a new margin statistics for the
characterization of margin distribution. Inspired by the theoretical findings,
we propose the MSVMAv, an efficient approach to achieve better performance by
optimizing margin distribution in terms of its empirical average margin and
semi-variance. We finally conduct extensive experiments to show the superiority
of the proposed MSVMAv approach.
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