Anzeige
Erschienen in:
2013 | OriginalPaper | Buchkapitel
Parallel Boosting with Momentum
verfasst von : Indraneel Mukherjee, Kevin Canini, Rafael Frongillo, Yoram Singer
Erschienen in: Machine Learning and Knowledge Discovery in Databases
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
We describe a new, simplified, and general analysis of a fusion of Nesterov’s accelerated gradient with parallel coordinate descent. The resulting algorithm, which we call BOOM, for
boo
sting with
m
omentum, enjoys the merits of both techniques. Namely, BOOM retains the momentum and convergence properties of the accelerated gradient method while taking into account the curvature of the objective function. We describe a
distributed
implementation of BOOM which is suitable for massive high dimensional datasets. We show experimentally that BOOM is especially effective in large scale learning problems with rare yet informative features.