Chen T, Ren J (2009) Bagging for Gaussian process regression. Neurocomputing 72:1605–1610Tao Chen , Jianghong Ren, Bagging for Gaussian process regression, Neurocomputing, v.72 n.7-9, p.1605-1610, March, 2009 [
Bagging for Gaussian Process Regression Tao Chen a,∗ , Jianghong Ren b a School of Chemical and Biomedical Engineering, Nanyang Technological University, Singapore 637459 b College of Automation, Chongqing University, Chongqing 400044, China Abstract This paper proposes the application of bagging to ...
For structured data, the comparative analysis includes SE-BLS, BLS [59], KBLS [13], BELM [12], Logistic Regression [60], Gaussian Naive Bayes [61], SVM with RBF kernel [62], Decision Tree [63], Bagging with trees [64], AdaBoost with trees [16], Random Forest [65], Extreme ...
I’m thinking, for example, of bagging and boosting forecast models. Or of the techniques that can be deployed for the problem of “many predictors,” techniques including principal component analysis, ridge regression, the lasso, and partial least squares. Probably one of the areas where these ...
In pattern recognition, the k-Nearest Neighbors algorithm (or simply k-NN) is a nonparametric approach for classification and regression22. The optimal choice of k depends on the distribution of the data. Typically, large k values may reduce the effect of noise on the classification23, but ...
Nonparametric estimation for big-but-biased data Article 26 January 2021 The reproducing kernel Hilbert space approach in nonparametric regression problems with correlated observations Article 01 October 2019 High-dimensional simultaneous inference with the bootstrap Article 09 October 2017 Explore...
The product of the wrinkle-free deformable area A wf (K) and the square √ root of the corresponding Gaussian curvature K is constant a wf for a given auxiliary material. Figure 10 shows the plot of the scalar measurements and the corresponding regression curves in relation to √K. ...
Bagging ensemble learning model based on Gaussian Process Regression is proposed to predict eddy current loss. The slot wedge conductivity, slot wedge of the relative permeability, rotor outer diameter and stator outer diameter are as the input of the prediction model, and the eddy current loss is...
Gaussian process regressionradar cross sectionshipsBootstrap aggregatingextrapolation. To solve the difficulty of obtaining a radar cross section (RCS) using traditional simulation and measurement methods under high frequency, this study proposes a hybrid method which combines bootstrap aggregation (Bagging)...
Plenty of ensemble-based proposals are among the state-of-the-art methods for the majority of supervised learning tasks [1], [6] (classification, regression, and ranking), and they have also succeeded at other learning tasks, such as preprocessing problems [10], [11], [12], data stream ...