Movatterモバイル変換


[0]ホーム

URL:


higrad: Statistical Inference for Online Learning and StochasticApproximation via HiGrad

Implements the Hierarchical Incremental GRAdient Descent (HiGrad) algorithm, a first-order algorithm for finding the minimizer of a function in online learning just like stochastic gradient descent (SGD). In addition, this method attaches a confidence interval to assess the uncertainty of its predictions. See Su and Zhu (2018) <doi:10.48550/arXiv.1802.04876> for details.

Version:0.1.0
Imports:Matrix
Published:2018-03-14
DOI:10.32614/CRAN.package.higrad
Author:Weijie Su [aut], Yuancheng Zhu [aut, cre]
Maintainer:Yuancheng Zhu <yuancheng.zhu at gmail.com>
License:GPL-3
NeedsCompilation:no
Materials:README,NEWS
CRAN checks:higrad results

Documentation:

Reference manual:higrad.html ,higrad.pdf

Downloads:

Package source: higrad_0.1.0.tar.gz
Windows binaries: r-devel:higrad_0.1.0.zip, r-release:higrad_0.1.0.zip, r-oldrel:higrad_0.1.0.zip
macOS binaries: r-release (arm64):higrad_0.1.0.tgz, r-oldrel (arm64):higrad_0.1.0.tgz, r-release (x86_64):higrad_0.1.0.tgz, r-oldrel (x86_64):higrad_0.1.0.tgz

Linking:

Please use the canonical formhttps://CRAN.R-project.org/package=higradto link to this page.


[8]ページ先頭

©2009-2025 Movatter.jp