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A Simple Multi-Class Boosting Framework with Theoretical Guarantees and Empirical Proficiency
Ron Appel · Pietro Perona

Tue Aug 08 01:30 AM -- 05:00 AM (PDT) @ Gallery #72

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.

Author Information

Ron Appel (caltech.edu)
Pietro Perona (caltech.edu)

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