Year: 2006
Type: preprint
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.
Source: arXiv (Cornell University)
Author: Kraemer, Nicole
Institution: Technische Universität Berlin
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W1838527790