Zobrazeno 1 - 10
of 907
pro vyhledávání: '"high-dimensional statistics"'
Publikováno v:
Geophysical Research Letters, Vol 50, Iss 16, Pp n/a-n/a (2023)
Abstract This study addresses how to model and predict large‐scale climate variability, such as the El Niño–Southern Oscillation (ENSO). We introduce a framework for inferring the macroscale causal structure of the climate system using a spatial
Externí odkaz:
https://doaj.org/article/bf96a393638247729a0c8d68287de301
Autor:
White, Alexander James
Indiana University-Purdue University Indianapolis (IUPUI)
The successful treatment and potential eradication of many complex diseases, such as cancer, begins with elucidating the convoluted mapping of molecular profiles to phenotypical manifesta
The successful treatment and potential eradication of many complex diseases, such as cancer, begins with elucidating the convoluted mapping of molecular profiles to phenotypical manifesta
Externí odkaz:
https://hdl.handle.net/1805/33295
Akademický článek
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Akademický článek
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Autor:
Wu, Yihong, Yang, Pengkun
Publikováno v:
The Annals of Statistics, 2019 Apr 01. 47(2), 857-883.
Externí odkaz:
https://www.jstor.org/stable/26581884
Autor:
Antonelli Joseph, Cefalu Matthew
Publikováno v:
Journal of Causal Inference, Vol 8, Iss 1, Pp 92-107 (2020)
There has been increasing interest in recent years in the development of approaches to estimate causal effects when the number of potential confounders is prohibitively large. This growth in interest has led to a number of potential estimators one co
Externí odkaz:
https://doaj.org/article/0b9d6a5660764a1aa70f609f1bb43ce3
Autor:
Muhammad Naveed Tabassum, Esa Ollila
Publikováno v:
IEEE Open Journal of Signal Processing, Vol 1, Pp 177-186 (2020)
We propose a compressive classification framework for settings where the data dimensionality is significantly larger than the sample size. The proposed method, referred to as compressive regularized discriminant analysis (CRDA), is based on linear di
Externí odkaz:
https://doaj.org/article/47c97fd8fe9a4cc59a7bb4df27d13c7b
Publikováno v:
The Annals of Statistics, 2018 Dec 01. 46(6B), 3603-3642.
Externí odkaz:
https://www.jstor.org/stable/26542913
Publikováno v:
Machine Learning: Science and Technology, Vol 4, Iss 2, p 025029 (2023)
Being able to reliably assess not only the accuracy but also the uncertainty of models’ predictions is an important endeavor in modern machine learning. Even if the model generating the data and labels is known, computing the intrinsic uncertainty
Externí odkaz:
https://doaj.org/article/26c1efbe1d28452ab5d3494938f78da9
Autor:
Elisabetta Cornacchia, Francesca Mignacco, Rodrigo Veiga, Cédric Gerbelot, Bruno Loureiro, Lenka Zdeborová
Publikováno v:
Machine Learning: Science and Technology, Vol 4, Iss 1, p 015019 (2023)
One of the most classical results in high-dimensional learning theory provides a closed-form expression for the generalisation error of binary classification with a single-layer teacher–student perceptron on i.i.d. Gaussian inputs. Both Bayes-optim
Externí odkaz:
https://doaj.org/article/0e933c7c1af445c798d726f853b9ee0a