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pro vyhledávání: '"Friedman, Jerome H"'
Autor:
Friedman, Jerome H.
The output of a machine learning algorithm can usually be represented by one or more multivariate functions of its input variables. Knowing the global properties of such functions can help in understanding the system that produced the data as well as
Externí odkaz:
http://arxiv.org/abs/2403.13141
Many regression and classification procedures fit a parameterized function $f(x;w)$ of predictor variables $x$ to data $\{x_{i},y_{i}\}_1^N$ based on some loss criterion $L(y,f)$. Often, regularization is applied to improve accuracy by placing a cons
Externí odkaz:
http://arxiv.org/abs/2107.07160
Autor:
Friedman, Jerome H.
The goal of regression analysis is to predict the value of a numeric outcome variable y given a vector of joint values of other (predictor) variables x. Usually a particular x-vector does not specify a repeatable value for y, but rather a probability
Externí odkaz:
http://arxiv.org/abs/2001.10102
Autor:
Friedman, Jerome H.
Often machine learning methods are applied and results reported in cases where there is little to no information concerning accuracy of the output. Simply because a computer program returns a result does not insure its validity. If decisions are to b
Externí odkaz:
http://arxiv.org/abs/1912.03785
Autor:
Friedman, Jerome H.
Publikováno v:
Proceedings of the National Academy of Sciences of the United States of America, 2020 Sep 01. 117(35), 21175-21184.
Externí odkaz:
https://www.jstor.org/stable/26968787
REPLY TO NOCK AND NIELSEN : Onthe work of Nock and Nielsen and its relationship to the additive tree
Autor:
Valdes, Gilmer, Luna, José Marcio, Gennatas, Efstathios D., Ungar, Lyle H., Eaton, Eric, Diffenderfer, Eric S., Jensen, Shane T., Simone, Charles B., Friedman, Jerome H., Solberg, Timothy D.
Publikováno v:
Proceedings of the National Academy of Sciences of the United States of America, 2020 Apr . 117(16), 8694-8695.
Externí odkaz:
https://www.jstor.org/stable/26929631
Autor:
Gennatas, Efstathios D., Friedman, Jerome H., Ungar, Lyle H., Pirracchio, Romain, Eaton, Eric, Reichmann, Lara G., Interian, Yannet, Luna, José Marcio, Simone, Charles B., Auerbach, Andrew, Delgado, Elier, van der Laan, Mark J., Solberg, Timothy D., Valdes, Gilmer
Publikováno v:
Proceedings of the National Academy of Sciences of the United States of America, 2020 Mar 01. 117(9), 4571-4577.
Externí odkaz:
https://www.jstor.org/stable/26929149
\texttt{rCOSA} is a software package interfaced to the R language. It implements statistical techniques for clustering objects on subsets of attributes in multivariate data. The main output of COSA is a dissimilarity matrix that one can subsequently
Externí odkaz:
http://arxiv.org/abs/1612.00259
Autor:
Luna, José Marcio, Gennatas, Efstathios D., Ungar, Lyle H., Eaton, Eric, Diffenderfer, Eric S., Jensen, Shane T., Simone, Charles B., Friedman, Jerome H., Solberg, Timothy D., Valdes, Gilmer
Publikováno v:
Proceedings of the National Academy of Sciences of the United States of America, 2019 Oct 01. 116(40), 19887-19893.
Externí odkaz:
https://www.jstor.org/stable/26857065
Autor:
Chen, Hao, Friedman, Jerome H.
Publikováno v:
Journal of the American Statistical Association 2017, Vol. 112, No. 517, 397-409, Theory and Methods
Two-sample tests for multivariate data and especially for non-Euclidean data are not well explored. This paper presents a novel test statistic based on a similarity graph constructed on the pooled observations from the two samples. It can be applied
Externí odkaz:
http://arxiv.org/abs/1307.6294