A simple multi-class boosting framework with theoretical guarantees and empirical proficiency
Year: 2017
Type: article
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.
Authors: Ron D. Appel, Pietro Perona
Institution: California Institute of Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: G2237907241, G4407244276, G8876996369
OpenAlex ID: W2739743715