Abstract
In this chapter we begin our discussion of some specific methods for supervised learning. These techniques each assume a (different) structured form for the unknown regression function, and by doing so they finesse the curse of dimensionality. Of course, they pay the possible price of misspecifying the model, and so in each case there is a tradeoff that has to be made. They take off where Chapters 3–6 left off. We describe five related techniques: generalized additive models, trees, multivariate adaptive regression splines, the patient rule induction method, and hierarchical mixtures of experts.
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© 2001 Springer Science+Business Media New York
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Hastie, T., Friedman, J., Tibshirani, R. (2001). Additive Models, Trees, and Related Methods. In: The Elements of Statistical Learning. Springer Series in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-0-387-21606-5_9
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DOI: https://doi.org/10.1007/978-0-387-21606-5_9
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4899-0519-2
Online ISBN: 978-0-387-21606-5
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