Results 351 to 360 of about 1,147,811 (393)

Ridge Regression: Applications to Nonorthogonal Problems

, 1970
This paper is an exposition of the use of ridge regression methods. Two examples from the literature are used as a base. Attention is focused on the RIDGE TRACE which is a two-dimensional graphical procedure for portraying the complex relationships in ...
A. E. Hoerl, R. Kennard
semanticscholar   +1 more source

Nucleation and evolution of ridge-ridge-ridge triple junctions: Thermomechanical model and geometrical theory

Tectonophysics, 2018
Abstract Ridge-ridge-ridge triple junctions are among the most remarkable features of global plate tectonics but their nucleation and evolution remains incompletely understood. Here, we employ 3D numerical models to study the processes of the nucleation and evolution of triple junctions induced by multi-directional lithospheric extension.
Gerya, Taras, Burov, Evgenii
openaire   +4 more sources

Ridge Estimators in Logistic Regression

, 1992
SUMMARY In this paper it is shown how ridge estimators can be used in logistic regression to improve the parameter estimates and to diminish the error made by further predictions. Different ways to choose the unknown ridge parameter are discussed.
S. Cessie, J. C. Houwelingen
semanticscholar   +1 more source

Generalized Inverses, Ridge Regression, Biased Linear Estimation, and Nonlinear Estimation

, 1970
A principal objective of this paper is to discuss a class of biased linear estimators employing generalized inverses. A second objective is to establish a unifying perspective.
Donald W. Marquaridt
semanticscholar   +1 more source

Approximation by Combinations of ReLU and Squared ReLU Ridge Functions With $\ell^1$ and $\ell^0$ Controls

IEEE Transactions on Information Theory, 2016
We establish $L^{\infty } $ and $L^{2} $ error bounds for functions of many variables that are approximated by linear combinations of rectified linear unit (ReLU) and squared ReLU ridge functions with $\ell ^{1} $ and $\ell ^{0} $ controls on ...
Jason M. Klusowski, A. Barron
semanticscholar   +1 more source

Ridge Shape as Influenced by Ridge Building Technique

Applied Engineering in Agriculture, 1996
Ridge height, ridge surface index (ratio of row width to ridge surface width), ridge cross-sectional area, and ridge form were measured to evaluate ridges formed during cultivation. Ridges were constructed with three tool types (disk, shovel, and sweep), operated at three speeds [5, 7, and 9 km/h (3, 5, and 7 mph)] and at three depths [5, 10, and 15 cm
Richard M. Cruse, D. C. Erbach, Mohammadreza Ghaffarzadeh   +2 more
openaire   +2 more sources

Ridge alterations following grafting of fresh extraction sockets in man. A randomized clinical trial.

Clinical Oral Implants Research, 2015
OBJECTIVE To evaluate dimensional alterations of the alveolar ridge that occurred following tooth extraction at sites grafted with Bio-Oss(®) Collagen. MATERIAL AND METHODS Twenty-eight subjects with maxillary incisors, canines, and premolars scheduled
M. Araújo, J. C. C. da Silva, Arthur Furtado de Mendonça, J. Lindhe   +3 more
semanticscholar   +1 more source

Global correlations of ocean ridge basalt chemistry with axial depth and crustal thickness

, 1987
Regional averages of the major element chemistry of ocean ridge basalts, corrected for low-pressure fractionation, correlate with regional averages of axial depth for the global system of ocean ridges, including hot spots, cold spots, and back arc basins,
E. Klein, C. Langmuir
semanticscholar   +1 more source

Ridge Regression

Computer Vision, 2013
T he familiar techniques of principal component regression (PCR) and partial least squares regression (PLSR) are both examples of what is called shrinkage estimation.
Tom Fearn
semanticscholar   +1 more source

Divide and conquer kernel ridge regression: a distributed algorithm with minimax optimal rates

Journal of machine learning research, 2013
We study a decomposition-based scalable approach to kernel ridge regression, and show that it achieves minimax optimal convergence rates under relatively mild conditions. The method is simple to describe: it randomly partitions a dataset of size N into m
Yuchen Zhang, John C. Duchi, M. Wainwright   +2 more
semanticscholar   +1 more source