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Additive Models, Trees, and Related Methods
[chapter]
2008
Springer Series in Statistics
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,
doi:10.1007/b94608_9
fatcat:2bpfwi3jtngubgnjeylxrbbuii