1 research outputs found
A Boosting Framework on Grounds of Online Learning
By exploiting the duality between boosting and online learning, we present a
boosting framework which proves to be extremely powerful thanks to employing
the vast knowledge available in the online learning area. Using this framework,
we develop various algorithms to address multiple practically and theoretically
interesting questions including sparse boosting, smooth-distribution boosting,
agnostic learning and some generalization to double-projection online learning
algorithms, as a by-product.Comment: Accepted in NIPS 201