Results 271 to 280 of about 3,667,438 (313)

Complete ensemble empirical mode decomposition hybridized with random forest and kernel ridge regression model for monthly rainfall forecasts

Journal of Hydrology, 2020
Persistent risks of extreme weather events including droughts and floods due to climate change require precise and timely rainfall forecasting. Yet, the naturally occurring non-stationarity entrenched within the rainfall time series lowers the model ...
Mumtaz Ali, R. Prasad, Yong Xiang, Z. Yaseen   +3 more
semanticscholar   +1 more source

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
openaire   +1 more source

A new ridge estimator for linear regression model with some challenging behavior of error term

Communications in statistics. Simulation and computation, 2023
Ridge regression is a variant of linear regression that aims to circumvent the issue of collinearity among predictors. The ridge parameter k has an important role in the bias-variance tradeoff.
Maha Shabbir, S. Chand, F. Iqbal
semanticscholar   +1 more source

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
openaire   +1 more source

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
openaire   +2 more sources

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.
openaire   +1 more source

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.
openaire   +2 more sources

Can CBOE gold and silver implied volatility help to forecast gold futures volatility in China? Evidence based on HAR and Ridge regression models

, 2020
The main purpose of this paper is to detect whether the CBOE gold and silver ETF (implied) volatility indices, i.e. GVZ and VXSLV, can help to forecast the realized volatility (RV) of gold futures price in China from both in-sample and out-of-sample ...
Yu Wei, Chao Liang, Yan Li, Xun Zhang, G. Wei   +4 more
semanticscholar   +1 more source

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
openaire   +1 more source

Prediction of compressive strength of GGBFS and Flyash-based geopolymer composite by linear regression, lasso regression, and ridge regression

Asian Journal of Civil Engineering, 2023
Ujjwal Sharma, N. Gupta, Manvendra Verma
semanticscholar   +1 more source