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搜索结果: 1-15 共查到Lasso相关记录74条 . 查询时间(0.131 秒)
With the Lutou Forest Farm as the research area, the Lasso algorithm was used for characteristic selection, and the optimal combination of variables was input into the support vector regression (SVR) ...
We consider actuator attacks in a remote control system. Compared with sensor attacks that have been studied in most existing literature, the malicious attacks over actuators can cause more disastrous...
Motivation: Differential network inference is a fundamental and challenging problem to reveal gene interactions and regulation relationships under different conditions. Many algorithms have been devel...
Lasso-logistic模型在医院下呼吸道感染预测中的应用     Lasso  医院感染  下呼吸道感染  风险评分  预测       font style='font-size:12px;'> 2020/5/29
建立住院患者医院下呼吸道感染预测模型,构建新的、简单的风险评分方法。方法 以2014年多家医院感染调查数据为训练集,建立住院患者医院下呼吸道感染的Lasso-logistic回归预测模型,选择贝叶斯信息准则(BIC)最小模型为最终模型,将回归系数放大相同倍数建立评分方法,以2015、2016年调查数据为验证集,并与文献建立的风险评分方法进行比较。
分析省级层面就医需求的政策变量和交互要素,并控制地区和时间效应的异质性,为精确估计医疗改革效应和医疗机构区域合理布局提供科学依据.以就医需求和就医供给的代理变量、区域特征控制变量建立指标体系,采用Post-double-selection-LASSO方法选择潜在变量及其函数形式.一阶差分、全控制变量和各省标准差集聚三个模型的比较结果显示,标准差集聚模型较好地控制时间趋势和初始差异,证实复杂就医需求...
On Pattern Recovery of The Fused Lasso     Confidence interval  jackknife empirical likelihood  risk measure       font style='font-size:12px;'> 2016/1/25
Quantifying risks is of importance in insurance. In this paper, we employ the jackknife empirical likelihood method to construct confidence intervals for some risk measures and related quantities stud...
This paper study sparse classification problems. We show that under single-index models, vanilla Lasso could give good estimate of unknown parameters. With this result, we see that even if the model i...
On Pattern Recovery of The Fused Lasso     Fused Lasso  Non-asymptotic  Pattern recovery  Preconditioning       font style='font-size:12px;'> 2016/1/20
We study the property of the Fused Lasso Signal Approximator(FLSA) for estimating a blocky signal sequence with additive noise.We transform the FLSA to an ordinary Lasso problem. By studying the prope...
The Lasso under Poisson-like Heteroscedasticity     Lasso  Poisson-like Model  Sign Consistency  Heteroscedas- ticity       font style='font-size:12px;'> 2016/1/19
The performance of the Lasso is well understood under the assumptions of the standard sparse linear model with homoscedastic noise. However, in several applications, the standard model does not descri...
本征音子说话人自适应方法在自适应数据量不足时会出现严重的过拟合现象,提出了一种基于稀疏组 LASSO 约束的本征音子说话人自适应算法。首先给出隐马尔可夫—高斯混合模型下本征音子说话人自适应的基 本原理;然后将稀疏组LASSO 正则化引入到本征音子说话人自适应,通过调整权重因子控制模型的复杂度,并 通过一种加速近点梯度的数学优化算法来实现;最后将稀疏组LASSO 约束的自适应算法与当前多种正则...
We consider the problem of learning a coefficient vector x0 ∈ RN from noisy linear observation y = Ax0 + w ∈ Rn. In many contexts (ranging from model selection to image processing) it is desirable to...
ON THE “DEGREES OF FREEDOM” OF THE LASSO     Degrees of freedom  LARS algorithm  lasso  model selection  SURE  unbiased estimate       font style='font-size:12px;'> 2015/8/21
We study the effective degrees of freedom of the lasso in the framework of Stein’s unbiased risk estimation (SURE). We show that the number of nonzero coefficients is an unbiased estimate for the degr...
Forward stagewise regression and the monotone lasso     regression  lasso  stagewise       font style='font-size:12px;'> 2015/8/21
We consider the least angle regression and forward stagewise algorithms for solving penalized least squares regression problems. In Efron,Hastie, Johnstone & Tibshirani (2004) it is proved that the le...
Sparse inverse covariance estimation with the lasso     Sparse inverse  covariance estimation  the lasso       font style='font-size:12px;'> 2015/8/21
We consider the problem of estimating sparse graphs by a lasso penalty applied to the inverse covariance matrix. Using a coordinate descent procedure for the lasso, we develop a simple algorithm— the ...
A note on the group lasso and a sparse group lasso     group lasso  sparse group lasso       font style='font-size:12px;'> 2015/8/21
We consider the group lasso penalty for the linear model. We note that the standard algorithm for solving the problem assumes that the model matrices in each group are orthonormal. Here we consider a ...

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