Optimal subsampling for softmax regression

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August undefined, 2024

WebApr 1, 2024 · They defined optimal subsampling probabilities by minimizing the asymptotic mean squared error (MSE) of the subsample-based estimator, and extracted sub-data … Web844, 2024b) developed an optimal subsampling method for logistic regression. The purpose of this paper is to extend their method to softmax regression, which is also called …

A Review on Optimal Subsampling Methods for Massive Datasets

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。 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 impd sgt. eric huxley

Optimal subsampling for quantile regression in big data

WebDec 18, 2024 · To meet the challenge of massive data, Wang et al. (J Am Stat Assoc 113(522):829–844, 2024b ) developed an optimal subsampling method for logistic … http://geekdaxue.co/read/johnforrest@zufhe0/qdms71 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 … impd slideshare

Optimal subsampling for softmax regression - Springer

Optimal subsampling for linear quantile regression models

Weboped an optimal subsampling method for logistic regression. The purpose of this paper is to extend their method to softmax regression, which is also called multinomial logistic … WebSoftmax regression, a generalization of Logistic re-gression (LR) in the setting of multi-class classi-cation, has been widely used in many machine learning applications. However, the performance of softmax regression is extremely sensitive to the presence of noisy data and outliers. To address this issue, we propose a model of robust softmax ... impd shirtsWebApr 1, 2024 · Abstract: The information-based optimal subdata selection (IBOSS) is a computationally efficient method to select informative data points from large data sets … list world cup winners cricket

"WebA two-stage optimal subsampling estimation for missing data problems with large-scale data Computing methodologies Machine learning Mathematics of computing Mathematical analysis Probability and statistics Statistical paradigms Theory of computation View Issue’s Table of Contents back " - Optimal subsampling for softmax regression

Optimal subsampling for softmax regression

$arXiv:2205.02718v1 [math.NA] 5 May 2024$

WebApr 6, 2024 · The theory encompasses and generalises most existing methods in the field of optimal subdata selection based on unequal probability sampling and inverse probability weighting, and derives optimality conditions for a general class of optimality criteria. Subsampling is commonly used to overcome computational and economical bottlenecks … WebThis method was named as optimal subsampling methods motivated 4 fromtheA-optimalitycriterion(OSMAC),andwasimprovedinWang(2024b)byadopt-5 ing unweighted target functions for subsamples and Poisson subsampling. In addition 6 to logistic regression, OSMAC was investigated to include softmax regression (Yao and

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WebFeb 1, 2024 · Furthermore, the optimal subsampling probabilities are derived according to the A-optimality criterion. It is shown that the estimator based on the optimal subsampling asymptotically achieves a smaller variance than that by the uniform random subsampling. WebJul 1, 2024 · The information-based optimal subdata selection (IBOSS) is a computationally efficient method to select informative data points from large data sets through processing full data by columns. However, when the volume of a data set is too large to be processed in the available memory of a machine, it is infeasible to implement the IBOSS procedure.

WebFor softmax regression, the optimal subsampling algorithm has been investigated in [1] under the baseline constraint, where one dimension of the multivariate response variable … WebMar 17, 2024 · This article focuses on quantile regression with massive data where the sample size n (greater than 1 0 6 in general) is extraordinarily large but the dimension d (smaller than 20 in general) is small. We first formulate the general subsampling procedure and establish the asymptotic property of the resultant estimator.

WebThis paper fills the gap by studying the subsampling method for a widely used missing data estimator, the augmented inverse probability weighting (AIPW) estimator. The response mean estimation problem with missing responses is discussed for illustration. A two-stage subsampling method is proposed via Poisson sampling framework. WebNov 5, 2024 · Title: Optimal Poisson Subsampling for Softmax Regression Authors: Yaqiong Yao, Jiahui Zou Award ID(s): 2105571 Publication Date: 2024-11-05 NSF-PAR ID: …

WebThe problem of variable selection in neural network regression models with dependent data is considered. In this framework, a test procedure based on the introduction of a measure for the variable re

WebApr 25, 2024 · Softmax Regression Model; Image by Author. First, we have flattened our 28x28 image into a vector of length 784, represented by x in the above image. Second, we calculate the linear part for each class → zc = wc.X + bc where, zc is the linear part of the c’th class and wc is the set of weights of the c’th class. bc is the bias for the c ... impds.nic.inWebSep 17, 2015 · On the basis of the criterion, we provide two novel subsampling methods, the optimal subsampling and the predictor- length subsampling methods. The predictor … list works ログイン sanyo-shokai.localWebOptimal subsampling for softmax regression Article Full-text available Apr 2024 Yaqiong Yao Haiying Wang To meet the challenge of massive data, Wang et al. (J Am Stat Assoc 113 (522):829–844,... impd shooting videoWebFor 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 impd southeast district police reportWebDec 1, 2024 · Wang H Ma Y Optimal subsampling for quantile regression in big data Biometrika 2024 108 1 99 112 4226192 10.1093/biomet/asaa043 1462.62248 Google Scholar; Wang H Zhu R Ma P Optimal subsampling for large sample logistic regression J. Am. Stat. Assoc. 2024 113 522 829 844 3832230 10.1080/01621459.2024.1292914 … impd south districtWebOptimal Subsampling for Softmax Regression. Statistical Papers Languages Chinese Native or bilingual proficiency English Professional … listworldsWebFor softmax regression, the optimal subsampling algorithm has been investigated in Yao and Wang (2024) under the baseline constraint, where one dimension of the multivariate … impd southeast district indianapolis