The problem of assessing the performance of a classifier, in the finite-sample setting, has been addressed by Vapnik in his seminal work by using data-independent measures of complexity. Recently, several authors have addressed the same problem by proposing data-dependent measures, which tighten previous results by taking in account the actual data distribution. In this framework, we derive some data- dependent bounds on the generalization ability of a classifier by exploiting the Rademacher Complexity and recent concentration results: in addition of being appealing for practical purposes, as they exploit empirical quantities only, these bounds improve previously known results.
An Improved Analysis of the Rademacher Data-dependent Bound Using Its Self-Bounding Property
ONETO, LUCA;GHIO, ALESSANDRO;ANGUITA, DAVIDE;RIDELLA, SANDRO
2013-01-01
Abstract
The problem of assessing the performance of a classifier, in the finite-sample setting, has been addressed by Vapnik in his seminal work by using data-independent measures of complexity. Recently, several authors have addressed the same problem by proposing data-dependent measures, which tighten previous results by taking in account the actual data distribution. In this framework, we derive some data- dependent bounds on the generalization ability of a classifier by exploiting the Rademacher Complexity and recent concentration results: in addition of being appealing for practical purposes, as they exploit empirical quantities only, these bounds improve previously known results.
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