Results 31 to 40 of about 3,667,438 (313)
Mendel, 2023 Can machine learning take a prediction to win an investment in ICO (Initial Coin Offering)? In this research work, our objective is to answer this question. Four popular and lower computational demanding approaches including Ridge regression (RR),
Toai Kim Tran +6 more
doaj +1 more source
An identity for kernel ridge regression [PDF]
Theoretical Computer Science, 2010 35 pages; extended version of ALT 2010 paper (Proceedings of ALT 2010, LNCS 6331, Springer, 2010)
Fedor Zhdanov, Yuri Kalnishkan
openaire +7 more sources
Nuclear masses in extended kernel ridge regression with odd-even effects [PDF]
, 2021 The kernel ridge regression (KRR) approach is extended to include the odd-even effects in nuclear mass predictions by remodulating the kernel function without introducing new weight parameters and inputs in the training network.
X. H. Wu, L. H. Guo, P. Zhao
semanticscholar +1 more source
Correlation Based Ridge Parameters in Ridge Regression with Heteroscedastic Errors and Outliers [PDF]
Journal of Statistical Theory and Applications (JSTA), 2015 This paper introduces some new estimators for estimating ridge parameter, based on correlation between response and regressor variables for ridge regression analysis.
A.V. Dorugade
doaj +1 more source
Low-Rank Tensor Thresholding Ridge Regression
IEEE Access, 2019 In the area of subspace clustering, methods combining self-representation and spectral clustering are predominant in recent years. For dealing with tensor data, most existing methods vectorize them into vectors and lose most of the spatial information ...
Kailing Guo +3 more
doaj +1 more source
Suggested Methods in Ridge Regression [PDF]
المجلة العراقية للعلوم الاحصائية, 2006 Three suggested procedures were adopted to determine the value of biasing parameter (k) in ridge regression: 1-fragmenting the ridge trace to groups each group contain semi-homogeneous absolute values of the estimated parameters, 2-rotating over the ...
doaj +1 more source
Adaptive ridge regression for rare variant detection. [PDF]
PLoS ONE, 2012 It is widely believed that both common and rare variants contribute to the risks of common diseases or complex traits and the cumulative effects of multiple rare variants can explain a significant proportion of trait variances.
Haimao Zhan, Shizhong Xu
doaj +1 more source
Mathematics, 2022 For the linear model Y=Xb+error, where the number of regressors (p) exceeds the number of observations (n), the Elastic Net (EN) was proposed, in 2005, to estimate b.
Rajaram Gana
doaj +1 more source
Logistic regression diagnostics in ridge regression
Computational Statistics, 2017 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
M. Revan Özkale +2 more
openaire +3 more sources
An Analogue In-Memory Ridge Regression Circuit With Application to Massive MIMO Acceleration
IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 2022 In-memory computing (IMC) has emerged as one of the most promising candidates for distributed computing frameworks such as edge computing, owing to its unrivalled energy efficiency and high throughput.
P. Mannocci, E. Melacarne, D. Ielmini
semanticscholar +1 more source