Regularized Regression Methods for Variable Selection and Estimation. DICKER L H. Journal of Women s Health . 2010DICKER L H. Regularized Regression Methods for Variable Selection and Estimation[D].Boston:Harvard Univer-sity 2010.Dicker, L. (2010), "Regularized regression methods for variable ...
Regularized regression techniques for linear regression have been created the last few ten years to reduce the flaws of ordinary least squares regression with regard to prediction accuracy. In this paper, new methods for using regularized regression in model choice are introduced, and we distinguish ...
Linear regression (LR) and some of its variants have been widely used for classification problems. Most of these methods assume that during the learning phase, the training samples can be exactly transformed into a strict binary label matrix, which has too little freedom to fit the labels adequa...
The split Bregman methods for L1 regularized problems An Interior-Point Method for Large-Scale l 1 -Regularized Logistic Regression An Interior-Point Method for Large-Scale l 1 -Regularized Logistic Regression Osher, S.: The split Bregman method for L1 regularized problems. SIAM J. Imaging Sci....
We propose to introduce a Gaussian prior distribution on the unknown parameters of the inverse regression problem in order to regularize their estimation. We show that some existing SIR regularizations can enter our framework, which permits a global understanding of these methods. Three new priors ...
Learning rates for the risk of kernel-based quantile regression estimators in additive models. Additive models play an important role in semiparametric statistics. This paper gives learning rates for regularized kernel-based methods for additive mode... Christmann,Andreas,Zhou,... - 《Analysis & Appl...
As an example, we used data from a culture media optimization study for microbial hydrogen production. All the three methods were capable in providing a significant model when the five variables of the culture media optimization were linearly included in modeling. However, multiple linear regression ...
“Methods”): an adversarial-regularized encoder, graph learning and fusion, and a GNN classifier. We first develop a multimodal adversarial-regularized encoder to cope with the feature heterogeneity of each modality and reduce the distributional divergence. Through the graph learning module, we then ...
On the Finite Time Convergence of Cyclic Coordinate Descent Methods In this paper we generalize the framework of the feasible descent method (FDM) to a randomized (R-FDM) and a coordinate-wise random feasible descent method... A Saha,A Tewari - 《Computer Science》 被引量: 52发表: 2010年...
However, current methods for integrating single-cell-level omics data with clinical variables are inadequate. In this study, we propose a regularized Bayesian Dirichlet-multinomial regression framework to investigate the relationship between single-cell RNA sequencing data and patient-level clinical data. ...