Boosting for Functional Data

Nicole Kraemer

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Boosting for Functional Data

Nicole Kraemer

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Abstract

We deal with the task of supervised learning if the data is of functional type. The crucial point is the choice of the appropriate fitting method (learner). Boosting is a stepwise technique that combines learners in such a way that the composite learner outperforms the single learner. This can be done by either reweighting the examples or with the help of a gradient descent technique. In this paper, we explain how to extend Boosting methods to problems that involve functional data.

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Nicole Kraemer. 2006-05-30. Boosting for Functional Data. https://arxiv.org/abs/math/0605751

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