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Fully corrective boosting with arbitrary loss and regularization
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DOI：10.1016/j.neunet.2013.07.006.png)
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Abstract
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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, l(p)-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. (C) 2013 Elsevier Ltd. All rights reserved.
Keywords:
Boosting
Ensemble learning
Convex optimization
Column generation
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