RI: Small: Boosting, Optimality and Game Theory

Princeton University, Princeton NJ

Investigators

Abstract

Boosting is a machine-learning method based on combining many carefully trained weak prediction rules into a single, highly accurate classifier. Boosting has both a rich theory and a record of empirical success, for instance, to face detection and spoken-dialogue systems. The theory of boosting is broadly connected to other research fields, but has only been fully developed for the simplest learning problems. Nevertheless, in practice, boosting is commonly applied in settings where the theory lags well behind. We do not know if such practical methods are truly best possible; even for binary classification, it is not clear how to best exploit what is known about how boosting operates. New challenges will demand an even greater widening of the foundations of boosting. The goal of this project is to develop broad theoretical insights and versatile algorithmic principles. The aim is to study game-theoretically how to design the most efficient and effective boosting algorithms possible. Research on boosting is spread over many years. across multiple publications and disciplines. To organize this body of work, a significant activity of this project is the completion of a book on boosting which will provide a valuable resource for students and researchers of diverse backgrounds and interests. Boosting has historically had a major impact on areas outside machine learning, such as statistics, computer vision, and speech and language processing. Thus, there is a strong potential for work at its foundations to have a broad impact on these other research and application areas as well.