ICML 2017poster10 citations
A Simple Multi-Class Boosting Framework with Theoretical Guarantees and Empirical Proficiency
Abstract
There is a need for simple yet accurate white-box learning systems that train quickly and with little data. To this end, we showcase REBEL, a multi-class boosting method, and present a novel family of weak learners called localized similarities. 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 consistent tendency to avoid overfitting. We evaluate our method on MNIST and standard UCI datasets against other state-of-the-art methods, showing the empirical proficiency of our method.
BibTeX
@InProceedings{pmlr-v70-appel17a,
title = {A Simple Multi-Class Boosting Framework with Theoretical Guarantees and Empirical Proficiency},
author = {Ron Appel and Pietro Perona},
booktitle = {Proceedings of the 34th International Conference on Machine Learning},
pages = {186--194},
year = {2017},
editor = {Precup, Doina and Teh, Yee Whye},
volume = {70},
series = {Proceedings of Machine Learning Research},
month = {06--11 Aug},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v70/appel17a/appel17a.pdf},
url = {https://proceedings.mlr.press/v70/appel17a.html},
abstract = {There is a need for simple yet accurate white-box learning systems that train quickly and with little data. To this end, we showcase REBEL, a multi-class boosting method, and present a novel family of weak learners called localized similarities. 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 consistent tendency to avoid overfitting. We evaluate our method on MNIST and standard UCI datasets against other state-of-the-art methods, showing the empirical proficiency of our method.}
}