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 $$\ell _2$$ context, we show that boosting with a weak learner defined by a kernel K is equivalent to estimation with a special boosting kernel. The number of boosting iterations can then be 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 general weak learners, including those based on the $$\ell _1$$ , hinge and Vapnik losses. We develop fast hyperparameter tuning for this class, which has a wide range of applications including robust regression and classification. We illustrate several applications using synthetic and real data.
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