Structural Risk Minimization over Data-Dependent Hierarchies

Structural Risk Minimization over Data-Dependent Hierarchies (1996)

Cached

Download Links

Other Repositories/Bibliography

DBLP

by John Shawe-taylor , Royal Holloway , Peter L. Bartlett , Robert C. Williamson , Martin Anthony

Citations:

221 - 50 self

BibTeX

@MISC{Shawe-taylor96structuralrisk,
    author = {John Shawe-taylor and Royal Holloway and Peter L. Bartlett and Robert C. Williamson and Martin Anthony},
    title = {Structural Risk Minimization over Data-Dependent Hierarchies},
    year = {1996}
}

Years of Citing Articles

Bookmark

OpenURL

Abstract

The paper introduces some generalizations of Vapnik's method of structural risk minimisation (SRM). As well as making explicit some of the details on SRM, it provides a result that allows one to trade off errors on the training sample against improved generalization performance. It then considers the more general case when the hierarchy of classes is chosen in response to the data. A result is presented on the generalization performance of classifiers with a "large margin". This theoretically explains the impressive generalization performance of the maximal margin hyperplane algorithm of Vapnik and co-workers (which is the basis for their support vector machines). The paper concludes with a more general result in terms of "luckiness" functions, which provides a quite general way for exploiting serendipitous simplicity in observed data to obtain better prediction accuracy from small training sets. Four examples are given of such functions, including the VC dimension measured on the samp...

Citations

No citations identified.