title: Fully corrective boosting with arbitrary loss and regularization.
abstract: We propose a general framework for analyzing and developing fully corrective boosting-based classifiers. The framework accepts any convex objective function, and allows any convex (for example, ℓp-norm, p ≥ 1) regularization term. By placing the wide variety of existing fully corrective boosting-based classifiers on a common footing, and considering the primal and dual problems together, the framework allows a direct comparison between apparently disparate methods. By solving the primal rather than the dual the framework is capable of generating efficient fully-corrective boosting algorithms without recourse to sophisticated convex optimization processes. We show that a range of additional boosting-based algorithms can be incorporated into the framework despite not being fully corrective. Finally, we provide an empirical analysis of the performance of a variety of the most significant boosting-based classifiers on a few machine learning benchmark datasets.
authors.affiliation: School of Computer Science, The University of Adelaide, Adelaide, SA 5005, Australia. Electronic address: chhshen@gmail.com.
authors.forename: Chunhua
authors.initials: C
authors.lastname: Shen
authors.forename: Hanxi
authors.initials: H
authors.lastname: Li
authors.forename: Anton
authors.initials: A
authors.lastname: van den Hengel
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016208:Databases, Factual; D004194:Disease; D015991:Epidemiologic Measurements; D006801:Humans
keywords: Boosting; Column generation; Convex optimization; Ensemble learning