Results 91 to 100 of about 52,512 (299)

Penalized Regression with Correlation Based Penalty [PDF]

, 2006
A new regularization method for regression models is proposed. The criterion to be minimized contains a penalty term which explicitly links strength of penalization to the correlation between predictors.
Gerhard Tutz, Jan Ulbricht, Tutz, Gerhard, Ulbricht, Jan   +3 more
core   +1 more source

Interpretable Machine Learning Framework for Nb─Si Based Alloy Design with Enhanced Fracture Toughness

Advanced Science, EarlyView.
An interpretable machine learning framework integrating SHAP and PDP analysis identifies critical design descriptors from 139 physicochemical features for Nb─Si alloys. The framework achieves <7% prediction error and guides the discovery of Nb38.5Ti38.5Si3Zr18V2 alloy with 22.791 MPa·m1/2 fracture toughness, breaking the 20 MPa·m1/2 barrier.
Dezhi Chen, Chao Xu, Jingyue Yu, Qi Wang, Hongze Fang, Shuo Yin, Turab Lookman, Ruirun Chen   +7 more
wiley   +1 more source

MODIFIED FUZZY-ROBUST RIDGE REGRESSION FOR MULTICOLLINEAR, OUTLIER-CONTAMINATED DATA

Science Journal of University of Zakho
Multicollinearity is known to have a significant impact on the stability of linear regression parameter estimation, while the presence of outliers tends to compound this problem. Ridge regression helps to improve the multicollinearity problem, but it is
Vaman M Salih, Shelan S Ismaeel
doaj   +1 more source

Adaptive Multivariate Ridge Regression

The Annals of Statistics, 1980
A multivariate version of the Hoerl-Kennard ridge regression rule is introduced. The choice from among a large class of possible generalizations is guided by Bayesian considerations; the result is implicitly in the work of Lindley and Smith although not actually derived there.
Brown, P. J., Zidek, J. V.
openaire   +2 more sources

Ridge Regression Learning Algorithm in Dual Variables

, 1998
In this paper we study a dual version of the Ridge Regression procedure. It allows us to perform non-linear regression by constructing a linear regression function in a high dimensional feature space.
C. Saunders, V. Vovk, Gammerman, A., Vovk, V., A. Gammerman, Saunders, C.   +5 more
core  

Ridge regression revisited [PDF]

, 2005
We argue in this paper that general ridge (GR) regression implies no major complication compared with simple ridge regression. We introduce a generalization of an explicit GR estimator derived by Hemmerle and by Teekens and de Boer and show that this ...
Hafner, CM (Christian), Boer, P.M.C. (Paul) de, de Boer, Paul, Hafner, C.M. (Christian)   +3 more
core   +1 more source

Artificial Intelligence‐Based Body Composition Analysis Reveals Sex‐Specific Prognostic Markers and Their Clinical Value in Gastric Cancer: A Multicenter Study

Advanced Science, EarlyView.
This study applied AI to quantify multidimensional body composition from CT images in gastric cancer and healthy controls. Distinct sex‐specific patterns and disease‐related alterations were identified and were associated with survival. Higher muscle and fat measures were linked to improved outcomes.
Tianxiang Li, Tingting Liu, Qian Yang, Zhiqing Zhang, Xiaofang Guo, Xiang Wang, Yihan Zhang, Limin Ge, Yu Su, Wenjun Wu, Weihua Guo, Zheng Wang, Lian Yang, Yanfeng Niu   +13 more
wiley   +1 more source

Difference based Ridge and Liu type Estimators in Semiparametric Regression Models [PDF]

We consider a difference based ridge regression estimator and a Liu type estimator of the regression parameters in the partial linear semiparametric regression model, y = Xβ + f + ε.
Wolfgang Karl Härdle, Maria Osipenko, Esra Akdeniz Duran   +2 more
core  

Reaction-Agnostic Featurization of Bidentate Ligands for Bayesian Ridge Regression of Enantioselectivity

, 2023
Chiral ligands are important components in asymmetric homogeneous catalysis, but their synthesis and screening can be both time-consuming and resource-intensive.
Jerome, Waser, Ruben, Laplaza, Alexandre, Schoepfer, Clemence, Corminboeuf, Matthew, Wodrich   +4 more
core   +1 more source

Construction of Sabatier Volcanoes for CO2 Hydrogenation to C1‐2 Oxygenates Using Data‐Efficient Machine Learning

Advanced Science, EarlyView.
A new data‐efficient framework combining DFT calculations, a neural network model, and automated graph analysis of catalytic reaction networks is proposed and applied to CO2 hydrogenation on transition metal nanoparticles. The analysis shows how efficient C2 oxygenate production requires a balance between CHx formation, C–C coupling, protonation, and ...
Mikhail V. Polynski, Sergey M. Kozlov
wiley   +1 more source