English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Early stopping of iterative algorithms is a widely-used form ofregularization in statistics, commonly used in conjunction with boosting andrelated gradient-type algorithms. Although consistency results have beenestablished in some settings, such estimators are less well-understood thantheir analogues based on penalized regularization. In this paper, for arelatively broad class of loss functions and boosting algorithms (includingL2-boost, LogitBoost and AdaBoost, among others), we exhibit a directconnection between the performance of a stopped iterate and the localizedGaussian complexity of the associated function class. This connection allows usto show that local fixed point analysis of Gaussian or Rademacher complexities,now standard in the analysis of penalized estimators, can be used to deriveoptimal stopping rules. We derive such stopping rules in detail for variouskernel classes, and illustrate the correspondence of our theory with practicefor Sobolev kernel classes.

Early stopping for kernel boosting algorithms: A general analysis with localized complexities