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A simple multi-class boosting framework with theoretical guarantees and empirical proficiency

Appel, Ron and Perona, Pietro (2017) A simple multi-class boosting framework with theoretical guarantees and empirical proficiency. Proceedings of Machine Learning Research, 70 . pp. 186-194. ISSN 1938-7228. https://resolver.caltech.edu/CaltechAUTHORS:20180627-133511664

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Abstract

There is a need for simple yet accurate white-box learning systems that train quickly and with lit- tle data. To this end, we showcase REBEL, a multi-class boosting method, and present a novel family of weak learners called localized similar- ities. Our framework provably minimizes the training error of any dataset at an exponential rate. We carry out experiments on a variety of synthetic and real datasets, demonstrating a con- sistent tendency to avoid overfitting. We eval- uate our method on MNIST and standard UCI datasets against other state-of-the-art methods, showing the empirical proficiency of our method.


Item Type:Article
Related URLs:
URLURL TypeDescription
http://proceedings.mlr.press/v70/appel17a.htmlPublisherArticle
ORCID:
AuthorORCID
Perona, Pietro0000-0002-7583-5809
Additional Information:© 2017 The author(s). The authors would like to thank anonymous reviewers for their feedback and Google Inc. and the Office of Naval Research MURI N00014-10-1-0933 for funding this work.
Funders:
Funding AgencyGrant Number
GoogleUNSPECIFIED
Office of Naval Research (ONR)N00014-10-1-0933
Record Number:CaltechAUTHORS:20180627-133511664
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20180627-133511664
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:87405
Collection:CaltechAUTHORS
Deposited By: Caroline Murphy
Deposited On:27 Jun 2018 20:50
Last Modified:03 Oct 2019 19:55

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