Optimal subsampling for softmax regression

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August undefined, 2024

WebDec 1, 2024 · Model constraints independent optimal subsampling probabilities for softmax regression December 2024 Authors: Yaqiong Yao Jiahui Zou Haiying Wang University of … WebJan 28, 2024 · The optimal subsampling methods have been investigated to include logistic regression models, softmax regression models, generalized linear models, quantile …

Model constraints independent optimal subsampling probabilities …

WebSubsampling techniques are efficient methods for handling big data. Quite a few optimal sampling methods have been developed for parametric models in which the loss … WebThe purpose of this paper is to extend their method to softmax regression, which is also called multinomial logistic regression and is commonly used to model data with multiple … toy story alien ship

Optimal Subsampling Approaches for Large Sample Linear …

Websubsampling least squares regression. On the other hand, several scholars have developed optimal subsampling methods for parametric regression problems. For example, Wang et al. (2024a) proposed an inverse weighted subsampling method for logistic regression based on the A- or L-optimality criterion. Subsequently, a more eﬃcient estimation WebApr 12, 2024 · Optimal Transport Minimization: Crowd Localization on Density Maps for Semi-Supervised Counting ... GEN: Pushing the Limits of Softmax-Based Out-of-Distribution Detection Xixi Liu · Yaroslava Lochman · Christopher Zach ... DARE-GRAM : Unsupervised Domain Adaptation Regression by Aligning Inverse Gram Matrices Ismail Nejjar · Qin … WebDec 4, 2024 · This thesis is concerned with massive data analysis via robust A-optimally efficient non-uniform subsampling. Motivated by the fact that massive data often contain outliers and that uniform sampling is not efficient, we give numerous sampling distributions by minimizing the sum of the component variances of the subsampling estimate. And … thermomix panettone recipe

AReviewonOptimalSubsamplingMethodsfor MassiveDatasets

Optimal subsampling for quantile regression in big data

WebFor softmax regression, the optimal subsampling algorithm has been investigated in [1] under the baseline constraint, where one dimension of the multivariate response variable is set as the baseline and the corresponding parameter is set to be a vector of zeros. With this constraint, the resulting optimal subsampling probabilities treat the Web这 725 个机器学习术语表，太全了！ Python爱好者社区 Python爱好者社区微信号 python_shequ 功能介绍人生苦短，我用Python。分享Python相关的技术文章、工具资源、精选课程、视频教程、热点资讯、学习资料等。 toy story alien socksWebThis idea was generalized in [11] to softmax regression. An optimal subsampling method under the A-optimality criterion (OSMAC) for logistic re-gression inspired by the idea of optimal design of experiments was developed in [12]. They proposed to use a pilot subsample to estimate the optimal subsampling probabilities, which toy story alien slippers

"For the softmax regression model with massive data, we have established the asymptotic normality of the general subsampling estimator, and then derived optimal subsampling probabilities under the A-optimality criterion and the L-optimality with a specific L. See more As N\rightarrow \infty , {\mathbf {M}}_N=N^{-1}\sum _{i=1}^{N}{\varvec{\phi }}_i({\hat{\varvec{\beta }}}_{{\mathrm {full}}})\otimes ({\mathbf {x}}_i{\mathbf {x}}_i^\mathrm{T}) goes to a positive-definite matrix in … See more In this theorem, both n and N go to infinity, but there are no restrictions on their relative orders. Even if n is larger than N, the theorem is still … See more For k=2, 4, N^{-2}\sum _{i=1}^{N}\pi _i^{-1}\Vert {\mathbf {x}}_i\Vert ^k=O_P(1); and there exists some \delta >0 such that N^{-(2+\delta )}\sum … See more Under Assumptions 1 and 2, given the full data {\mathcal {D}}_N in probability, as n\rightarrow \infty and N\rightarrow \infty , the approximation error {\hat{\varvec{\beta … See more " - Optimal subsampling for softmax regression

Model constraints independent optimal subsampling probabilities …

Optimal Subsampling Approaches for Large Sample Linear …

Optimal subsampling for softmax regression

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