Results 271 to 280 of about 28,744 (310)
Some of the next articles are maybe not open access.
Error Analysis for Matrix Elastic-Net Regularization Algorithms
IEEE Transactions on Neural Networks and Learning Systems, 2012Elastic-net regularization is a successful approach in statistical modeling. It can avoid large variations which occur in estimating complex models. In this paper, elastic-net regularization is extended to a more general setting, the matrix recovery (matrix completion) setting.
Luoqing Li
exaly +3 more sources
Regularization and Variable Selection Via the Elastic Net [PDF]
SummaryWe propose the elastic net, a new regularization and variable selection method. Real world data and a simulation study show that the elastic net often outperforms the lasso, while enjoying a similar sparsity of representation. In addition, the elastic net encourages a grouping effect, where strongly correlated predictors tend to be in or out of ...
Hui Zou, Trevor Hastie
exaly +2 more sources
Type-1 fuzzy forecasting functions with elastic net regularization
Fuzzy functions have recently been used for forecasting problems. The main concepts behind a fuzzy functions are to cluster the inputs using a fuzzy clustering method and to include the obtained membership grades and their non-linear transformations as new variables in the input matrix.
Nihat Tak, Deniz Inan
exaly +3 more sources
Learning performance of elastic-net regularization
In this paper, within the framework of statistical learning theory we address the elastic-net regularization problem. Based on the capacity assumption of hypothesis space composed by infinite features, significant contributions are made in several aspects.
Zhao, Yu-long, Feng, Yun-long
exaly +2 more sources
Generalized conditional gradient method for elastic-net regularization
Journal of Computational and Applied Mathematics, 2022zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Hailong Li, Liang Ding
exaly +2 more sources
ELASTIC-NET REGULARIZATION FOR LOW-RANK MATRIX RECOVERY
International Journal of Wavelets, Multiresolution and Information Processing, 2012This paper considers the problem of recovering a low-rank matrix from a small number of measurements consisting of linear combinations of the matrix entries. We extend the elastic-net regularization in compressive sensing to a more general setting, the matrix recovery setting, and consider the elastic-net regularization scheme for matrix recovery.
Li, Hong, Chen, Na, Li, Luoqing
openaire +1 more source
Elastic Net Regularization in Diffuse Optical Tomography Applications
2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), 2019Diffuse optical tomography (DOT) uses near-infrared light to obtain quantitative information about the optical coefficients in biological tissues. Such an information can be clinically exploited for diagnostic purposes. In DOT, the surface of the investigated tissue is illuminated with a light source and the emerging light is measured at various ...
Causin P., Naldi G., Weishaeupl R. M.
openaire +2 more sources
Elastic-Net Regularization: Iterative Algorithms and Asymptotic Behavior of Solutions
Numerical Functional Analysis and Optimization, 2010We derive strongly convergent algorithms to solve inverse problems involving elastic-net regularization. Moreover, using functional analysis techniques, we provide a rigorous study of the asymptotic properties of the regularized solutions that allows to cast in a unified framework l1, elastic-net and classical Tikhonov regularization.
Veronica Umanità, Silvia Villa
exaly +3 more sources
Elastic Net Regularization in Lorentz force evaluation
NDT & E International, 2018Abstract Lorentz force evaluation is a nondestructive evaluation method applied for the characterization of sub-surface defects in specimen consisting of layers of conducting material. The movement of a specimen under investigation relative to a permanent magnet leads to Lorentz forces that are perturbed in the presence of a defect.
E.-M. Dölker +8 more
openaire +1 more source

