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

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.