Rademacher Complexity Bounds for Non-I.I.D. Processes

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Rademacher Complexity Bounds for Non-I.I.D. Processes
This paper presents the first Rademacher complexity-based error bounds for noni.i.d. settings, a generalization of similar existing bounds derived for the i.i.d. case. Our bounds hold in the scenario of dependent samples generated by a stationary -mixing process, which is commonly adopted in many previous studies of noni.i.d. settings. They benefit from the crucial advantages of Rademacher complexity over other measures of the complexity of hypothesis classes. In particular, they are data-dependent and measure the complexity of a class of hypotheses based on the training sample. The empirical Rademacher complexity can be estimated from such finite samples and lead to tighter generalization bounds. We also present the first margin bounds for kernel-based classification in this non-i.i.d. setting and briefly study their convergence.
Mehryar Mohri, Afshin Rostamizadeh
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2008
Where NIPS
Authors Mehryar Mohri, Afshin Rostamizadeh
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