A Simple Multi-Class Boosting Framework with Theoretical Guarantees and Empirical Proficiency

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Ron Appel, Pietro Perona ;
Proceedings of the 34th International Conference on Machine Learning, PMLR 70:186-194, 2017.

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.

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