Boosting as a kernel-based method
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Boosting combines weak (biased) learners to obtain effective learning algorithms for classification and prediction. In this paper, we show a connection between boosting and kernel-based methods, highlighting both theoretical and practical applications. In the context of ℓ_2 boosting, we start with a weak linear learner defined by a kernel K. We show that boosting with this learner is equivalent to estimation with a special boosting kernel that depends on K, as well as on the regression matrix, noise variance, and hyperparameters. The number of boosting iterations is modeled as a continuous hyperparameter, and fit along with other parameters using standard techniques. We then generalize the boosting kernel to a broad new class of boosting approaches for more general weak learners, including those based on the ℓ_1, hinge and Vapnik losses. The approach allows fast hyperparameter tuning for this general class, and has a wide range of applications, including robust regression and classification. We illustrate some of these applications with numerical examples on synthetic and real data.
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