Deep boosting

Corinna Cortes, Mehryar Mohri, Umar Syed

Research output: Chapter in Book/Report/Conference proceedingConference contribution


We present a new ensemble learning algorithm, DeepBoost, which can use as base classifiers a hypothesis set containing deep decision trees, or members of other rich or complex families, and succeed in achieving high accuracy without over- fitting the data. The key to the success of the al-gorithm is a capacity-conscious criterion for the selection of the hypotheses. We give new data- dependent learning bounds for convex ensembles expressed in terms of the Rademacher complexi-ties of the sub-families composing the base classifier set, and the mixture weight assigned to each sub-family. Our algorithm directly benefits from these guarantees since it seeks to minimize the corresponding learning bound. We give a full description of our algorithm, including the details of its derivation, and report the results of several experiments showing that its performance compares favorably to that of AdaBoost and Logistic Regression and their Li -regularized variants.

Original languageEnglish (US)
Title of host publication31st International Conference on Machine Learning, ICML 2014
PublisherInternational Machine Learning Society (IMLS)
Number of pages14
ISBN (Electronic)9781634393973
StatePublished - 2014
Event31st International Conference on Machine Learning, ICML 2014 - Beijing, China
Duration: Jun 21 2014Jun 26 2014

Publication series

Name31st International Conference on Machine Learning, ICML 2014


Other31st International Conference on Machine Learning, ICML 2014

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Software


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