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pro vyhledávání: '"Meyer, Joseph T."'
This paper derives a unifying theorem establishing consistency results for a broad class of tree-based algorithms. It improves current results in two aspects. First of all, it can be applied to algorithms that vary from traditional Random Forests due
Externí odkaz:
http://arxiv.org/abs/2309.01460
We consider a global representation of a regression or classification function by decomposing it into the sum of main and interaction components of arbitrary order. We propose a new identification constraint that allows for the extraction of interven
Externí odkaz:
http://arxiv.org/abs/2208.06151
Autor:
Meyer, Joseph T.
We establish optimal convergence rates up to a log-factor for a class of deep neural networks in a classification setting under a restraint sometimes referred to as the Tsybakov noise condition. We construct classifiers in a general setting where the
Externí odkaz:
http://arxiv.org/abs/2207.12180
We discuss local linear smooth backfitting for additive non-parametric models. This procedure is well known for achieving optimal convergence rates under appropriate smoothness conditions. In particular, it allows for the estimation of each component
Externí odkaz:
http://arxiv.org/abs/2201.10930
We introduce a novel interpretable tree based algorithm for prediction in a regression setting. Our motivation is to estimate the unknown regression function from a functional decomposition perspective in which the functional components correspond to
Externí odkaz:
http://arxiv.org/abs/2012.14563
Akademický článek
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Autor:
Kim, Robert Y. 1, Meyer, Joseph T., Spencer, Sharon A., Meredith, Ruby F., Jennelle, Richard L.S., Salter, Merle M.
Publikováno v:
In International Journal of Radiation Oncology, Biology, Physics 1996 35(5):1035-1038
Autor:
Meyer, Joseph T., Hixson, William C., Jennelle, Richard L.S., Alonso, Jorge E., Raben, David, Spencer, Sharon S., Kim, Robert Y.
Publikováno v:
In International Journal of Radiation Oncology, Biology, Physics 1996 36(1) Supplement 1:360-360