Results 1 to 10 of about 5,204,898 (289)
Deep Residual Learning for Nonlinear Regression [PDF]
Entropy, 2020 Deep learning plays a key role in the recent developments of machine learning. This paper develops a deep residual neural network (ResNet) for the regression of nonlinear functions.
Dongwei Chen +3 more
doaj +5 more sources
Spiking neural networks for nonlinear regression [PDF]
Royal Society Open Science, 2022 Spiking neural networks (SNN), also often referred to as the third generation of neural networks, carry the potential for a massive reduction in memory and energy consumption over traditional, second-generation neural networks. Inspired by the undisputed
Alexander Henkes +2 more
doaj +4 more sources
Densely Connected Neural Networks for Nonlinear Regression [PDF]
Entropy, 2022 Densely connected convolutional networks (DenseNet) behave well in image processing. However, for regression tasks, convolutional DenseNet may lose essential information from independent input features.
Chao Jiang +3 more
doaj +2 more sources
Likelihood interval for nonlinear regression. [PDF]
Transl Clin Pharmacol, 2023 Wald confidence interval has been used as the conventional method of interval estimation for the parameters in nonlinear models. Because Wald confidence interval is symmetric around the point estimate, it does not reflect the asymmetry of the likelihood profile in nonlinear regression.
Lee MH, Bae KS.
europepmc +3 more sources
On Nonlinear Regression for Trends in Split-Belt Treadmill Training [PDF]
Brain Sciences, 2020 Single and double exponential models fitted to step length symmetry series are used to evaluate the timecourse of adaptation and de-adaptation in instrumented split-belt treadmill tasks.
Usman Rashid +4 more
doaj +2 more sources
Maximal Associated Regression: A Nonlinear Extension to Least Angle Regression
IEEE Access, 2021 This paper proposes Maximal Associated Regression (MAR), a novel algorithm that performs forward stage-wise regression by applying nonlinear transformations to fit predictor covariates.
Sanush K. Abeysekera +3 more
doaj +1 more source
Bioengineering, 2023 Photoplethysmography (PPG) signals are widely used in clinical practice as a diagnostic tool since PPG is noninvasive and inexpensive. In this article, machine learning techniques were used to improve the performance of classifiers for the detection of ...
Sivamani Palanisamy, Harikumar Rajaguru
doaj +1 more source
, 2023 Nonlinear regression analysis is a popular and important tool for scientists and engineers. In this article, we introduce theories and methods of nonlinear regression and its statistical inferences using the frequentist and Bayesian statistical modeling and computation.
Huang, Hsin-Hsiung, He, Qing
openaire +2 more sources
Adaptive Robust Regression by Using a Nonlinear Regression Program
Journal of Statistical Software, 1999 Robust regression procedures have considerable attention in mathematical statistics literature. They, however, have not received nearly as much attention by practitioners performing data analysis.
Mortaza Jamshidian
doaj +3 more sources
Nonlinear Regression With Censored Data [PDF]
Technometrics, 2007 Suppose that the random vector (X, Y) satisfies the regression model Y = m(X) + σ(X)ϵ, where m (·) = E(Y||·) belongs to some parametric class {mθ(·):θ∈} of regression functions, σ2(·) = var(Y||·) is unknown, and ϵ is independent of X. The response Y is subject to random right censoring, and the covariate X is completely observed.
Cédric Heuchenne, Ingrid Van Keilegom
openaire +3 more sources