Boosting for Functional Data

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dc.creatorKraemer, Nicole
dc.date2006-05-30
dc.date.accessioned2026-07-07T08:07:52Z
dc.date.available2026-07-07T08:07:52Z
dc.descriptionWe 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.
dc.identifierhttps://arxiv.org/abs/math/0605751
dc.identifierhttp://arxiv.org/abs/math/0605751
dc.identifierProceedings of the 17th International Conference on Computational Statistics, 2006, pp. 1121-1128
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/131076
dc.subjectStatistics Theory
dc.subject62G05;62M20;62G08
dc.titleBoosting for Functional Data
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