Results 151 to 160 of about 15,501 (203)

Thrombi from stroke due to cardioembolic etiology have higher CD11b-positive cells compared to large artery atherosclerosis. [PDF]

open access: yesJ Thromb Thrombolysis
Finger C   +9 more
europepmc   +1 more source

Enhanced thermoelectric performance in Fe<sub>2</sub>V<sub>0.8</sub>W<sub>0.2</sub>Al thin films: synergistic effects of chemical ordering and tungsten substitution.

open access: yesJ Mater Chem A Mater
Domínguez-Vázquez JM   +9 more
europepmc   +1 more source

Personalized biomarkers of multiscale functional alterations in temporal lobe epilepsy. [PDF]

open access: yesNat Commun
Xie K   +24 more
europepmc   +1 more source

Recent cloud trends and extremes reaffirm moderate climate sensitivity

open access: yes
Zelinka M   +9 more
europepmc   +1 more source

Multilinear Jointly Sparse Robust Discriminant Regression

Proceedings of the 2020 9th International Conference on Computing and Pattern Recognition, 2020
Tensor data, such as image, video, etc. is drawing more and more attention from researchers. Therefore, in this paper, we will focus on the tensor data, proposing a novel tensor-based feature extraction model. Previously, Lai et al. proposed Robust Discriminant Regression (RDR) by using L2, 1 -norm as basic metric to improve the robustness of model ...
Zhuozhen Yu, Zhihui Lai, Yusheng Lai
openaire   +1 more source

Multivariate Multilinear Regression

IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2012
Conventional regression methods, such as multivariate linear regression (MLR) and its extension principal component regression (PCR), deal well with the situations that the data are of the form of low-dimensional vector. When the dimension grows higher, it leads to the under sample problem (USP): the dimensionality of the feature space is much higher ...
Su, Ya   +3 more
openaire   +3 more sources

Correct and incorrect use of multilinear regression

Chemometrics and Intelligent Laboratory Systems, 1995
Abstract Multilinear regression is applied when experimenters wish to investigate the relationship between a block of predictor variables ( X ), whose values are fixed by the experimenter, and one or more responses ( Y ), measured at each experiment.
M. Sergent   +3 more
openaire   +3 more sources

Clinical risk prediction with multilinear sparse logistic regression

Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, 2014
Logistic regression is one core predictive modeling technique that has been used extensively in health and biomedical problems. Recently a lot of research has been focusing on enforcing sparsity on the learned model to enhance its effectiveness and interpretability, which results in sparse logistic regression model.
Fei Wang   +4 more
openaire   +1 more source

Further Multilinear Regression

2010
For one regressor x, simple linear regression is fine for fitting straight-line trends. But what about more general trends – quadratic trends, for example? (E.g. height against time for a body falling under gravity is quadratic.) Or cubic trends? (E.g.: the van der Waals equation of state in physical chemistry.) Or quartic? – etc.
N. H. Bingham, John M. Fry
openaire   +1 more source

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