The Structural Risk Minimization principle allows estimating the generalization ability of a learned hypothesis by measuring the complexity of the entire hypothesis class. Two of the most recent and effective complexity measures are the Rademacher Complexity and the Maximal Discrepancy, which have been applied to the derivation of generalization bounds for kernel classifiers. In this work, we extend their application to the regression framework.
Structural Risk Minimization and Rademacher Complexity for Regression
ANGUITA, DAVIDE;GHIO, ALESSANDRO;ONETO, LUCA;RIDELLA, SANDRO
2012-01-01
Abstract
The Structural Risk Minimization principle allows estimating the generalization ability of a learned hypothesis by measuring the complexity of the entire hypothesis class. Two of the most recent and effective complexity measures are the Rademacher Complexity and the Maximal Discrepancy, which have been applied to the derivation of generalization bounds for kernel classifiers. In this work, we extend their application to the regression framework.
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