⌘+K

Pulse Explore Communities Researchers Journals

Home Communities Explore

December 1, 2004Journal of Machine Learning Research

Boosting as a Regularized Path to a Maximum Margin Classifier

RossetSaharon ZhuJi HastieTrevor

View at Publisher

Ask AI

Bookmark

Share

Abstract

In this paper we study boosting methods from a new perspective. We build on recent work by Efron et al. to show that boosting approximately (and in some cases exactly) minimizes its loss criterion ...

Discussion

No takes yet. Share an insight, caveat, or question.

Cite This Study

RossetSaharon et al. (2004) studied this question.

synapsesocial.com/papers/6a733f8aa7d283abeec236c9 https://doi.org/10.5555/1005332.1016790

View at Publisher

Ask AI

Bookmark

Share

Keep reading

Synapse has enriched 5 related papers. Consider them for comparative context:

1Adaptive Online Prediction by Following the Perturbed Leader.Journal of Machine Learning Research · Thu,
2MEKA: A multi-label/multi-target extension to WekaJournal of Machine Learning Research · Fri,
3River: Machine learning for streaming data in pythonJournal of Machine Learning Research · Tue,
4An Introduction to Artificial Prediction Markets for ClassificationJournal of Machine Learning Research · Mon,
5Pattern Recognition for Conditionally Independent DataJournal of Machine Learning Research · Thu,

More from Journal of Machine Learning Research

Marginal Likelihood Integrals for Mixtures of Independence ModelsWed,
Undercomplete Blind Subspace DeconvolutionWed,
Nonlinear Models Using Dirichlet Process MixturesWed,
Matrix Completion from Noisy EntriesTue,
Manifold Learning: The Price of NormalizationTue,