Results 271 to 280 of about 52,512 (299)

Choquet Integral Ridge Regression

2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2020
The Choquet integral (ChI) is an aggregation function that is defined with respect to a fuzzy measure (FM). Many ChI-based decision aggregation methods have been proposed to learn the underlying FM. However, FM's boundary and monotonicity constraints have limited the applicability of such methods to decision-level fusion.
Siva K. Kakula, Anthony J. Pinar, Timothy C. Havens, Derek T. Anderson   +3 more
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Variations on Ridge Traces in Regression

Communications in Statistics - Simulation and Computation, 2012
Ridge regression, perturbing the design moment matrix via a parameter k, persists in the study of ill-conditioned systems. Ridge traces, exhibiting solutions as functions of k, are intended to reflect stability as k evolves, in contrast to transient instabilities in ordinary least squares.
Donald R. Jensen, Donald E. Ramirez
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Ridge Regression: A Historical Context

Technometrics, 2020
Two classical articles on Ridge Regression by Arthur Hoerl and Robert Kennard were published in Technometrics in 1970, making 2020 their 50th anniversary.
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Heteroscedastic kernel ridge regression

Neurocomputing, 2004
In this paper we extend a form of kernel ridge regression (KRR) for data characterised by a heteroscedastic (i.e. input dependent variance) Gaussian noise process, introduced in Foxall et al. (in: Proceedings of the European Symposium on Artificial Neural Networks (ESANN-2002), Bruges, Belgium, April 2002, pp. 19–24).
Cawley, Gavin C., Talbot, Nicola L. C., Foxall, Robert J., Dorling, Stephen R., Mandic, Danilo P.   +4 more
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Kernel Ridge Regression

2013
This chapter discusses the method of Kernel Ridge Regression, which is a very simple special case of Support Vector Regression. The main formula of the method is identical to a formula in Bayesian statistics, but Kernel Ridge Regression has performance guarantees that have nothing to do with Bayesian assumptions.
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Ridge Estimators in Logistic Regression

Applied Statistics, 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. The main attention focuses on ridge parameters obtained by cross-validation.
le Cessie, S., van Houwelingen, J. C.
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Kernel ridge regression classification

2014 International Joint Conference on Neural Networks (IJCNN), 2014
We present a nearest nonlinear subspace classifier that extends ridge regression classification method to kernel version which is called Kernel Ridge Regression Classification (KRRC). Kernel method is usually considered effective in discovering the nonlinear structure of the data manifold.
Jinrong He, Lixin Ding, Lei Jiang 0007, Ling Ma   +3 more
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ON THE CHOICE OF THE PARAMETER OF THE RIDGE REGRESSION

Far East Journal of Theoretical Statistics, 2015
Summary: Ridge regression, which defines a class of estimators indexed by a biasing parameter \(k\), is an alternative to the ordinary least squares (OLS) estimator in the multiple linear regression model. In this paper, the problem of choosing the biasing ridge regression parameter \(k\) is considered. Two methods of specifying \(k\) are proposed here
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Ridge Regression

2021
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Performance of Some New Ridge Regression Estimators

Communications in Statistics Part B: Simulation and Computation, 2003
B M Golam Kibria
exaly