Results 91 to 100 of about 177,424 (286)
Sensor-Based Continuous Authentication Using Cost-Effective Kernel Ridge Regression
IEEE Access, 2018 People prefer to store important, private, and sensitive information on smartphones for convenient storage and fast access, such as photos and emails.
Yantao Li +3 more
doaj +1 more source
An Identity for Kernel Ridge Regression [PDF]
, 2011 This paper derives an identity connecting the square loss of ridge regression in on-line mode with the loss of the retrospectively best regressor. Some corollaries about the properties of the cumulative loss of on-line ridge regression are also obtained ...
Kalnishkan, Yuri, Zhdanov, Fedor
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Advanced Science, EarlyView.Protease‐activated plasmonic nanosensors are demonstrated to noninvasively monitor antitumor T cell activity in tumors with anatomical information upon adoptive T cell transfer via ultrasound‐guided photoacoustic imaging. These nanosensors are actuated by granzyme B, a protease secreted by activated cytotoxic T cells during tumor cell killing, which ...
Myeongsoo Kim +10 more
wiley +1 more source
Takeuchi's Information Criteria as a form of Regularization
, 2018 Takeuchi's Information Criteria (TIC) is a linearization of maximum likelihood estimator bias which shrinks the model parameters towards the maximum entropy distribution, even when the model is mis-specified.
Dixon, Matthew, Ward, Tyler
core +1 more source
Advanced Science, EarlyView.IL27RA upregulation drives immune evasion in TNBC by suppressing MHC‐I expression and reprogramming T/NK‐cell states, establishing an immune‐excluded tumor phenotype. Targeting this epithelial‐intrinsic IL27RA–PI3K/AKT axis offers a promising strategy to overcome immunotherapy resistance.
Jiachi Xu +8 more
wiley +1 more source
Performance of Some Ridge Parameters for Probit Regression: with Application on Swedish Job Search Data [PDF]
In ridge regression the estimation of the ridge parameter is an important issue. This paper generalizes some methods for estimating the ridge parameter for probit ridge regression (PRR) model based on the work of Kibria et al. (2011).
Locking, Håkan +2 more
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Fast Ridge Regression with Randomized Principal Component Analysis and Gradient Descent [PDF]
, 2014 We propose a new two stage algorithm LING for large scale regression problems. LING has the same risk as the well known Ridge Regression under the fixed design setting and can be computed much faster. Our experiments have shown that LING performs well in
Foster, Dean P., Lu, Yichao
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Advanced Science, EarlyView.Donor‐derived tdTomato+ mature hepatocytes were FACS‐isolated and transplanted into Fah−/− host mice. During regeneration, these cells convert into proliferative, unipotent Afp+ rHeps. Their plasticity is governed by a PPARγ/AFP‐dependent metabolic switch, segregating into pro‐proliferative Afplow and pro‐survival Afphigh subpopulations.
Ting Fang +12 more
wiley +1 more source
Employing Ridge Regression Procedure to Remedy the Multicollinearity Problem
Ibn Al-Haitham Journal for Pure and Applied Sciences, 2017 In this paper we introduce many different Methods of ridge regression to solve multicollinearity problem in linear regression model. These Methods include two types of ordinary ridge regression (ORR1), (ORR2) according to the choice of ridge ...
Hazim M. Gorgees, Bushra A. Ali
doaj
Energy GeoscienceIn oil and gas exploration, elucidating the complex interdependencies among geological variables is paramount. Our study introduces the application of sophisticated regression analysis method at the forefront, aiming not just at predicting geophysical ...
Yang Li +5 more
doaj +1 more source