搜索结果: 1-15 共查到“Lasso”相关记录74条 . 查询时间(0.062 秒)
Remote Sensing Estimation of Forest Aboveground Biomass Based on Lasso-SVR
aboveground biomass remote sensing estimation Lasso algorithm support vector regression model
2023/12/1
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) ...
Academy of Mathematics and Systems Science, CAS Colloquia & Seminars：A LASSO-based Detection and Identification Method on Actuator Integrity Attacks in Remote Control Systems
LASSO 远程控制系统 执行器 完整性 攻击检测 识别方法
2023/4/28
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...
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。
基于Post-LASSO方法的就医需求多控制变量选择
就医 工具变量 交互要素 最小绝对收缩和选择算子(LASSO)
2018/11/15
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
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...
Vanilla Lasso for sparse classification under single index models
Vanilla Lasso sparse classification single index models
2016/1/20
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
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
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...
基于稀疏组LASSO约束的本征音子说话人自适应
说话人自适应 本征音子 组稀疏约束 稀疏组LASSO 约束 近点梯度法
2015/12/21
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...
The LASSO risk: asymptotic results and real world examples
Coefficient vector linear observation construct sparse the lasso matrix sequence
2015/8/21
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
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...
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
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 ...
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 ...