2022 Elsevier LtdWhen modelling censored observations (i.e. data in which the value of a measurement or observation is un-observable beyond a given threshold), a typical approach in current regression methods is to use a censored-Gaussian (i.e. Tobit) model to describe the conditional output ...
Sparse Convolved Multiple Output Gaussian Processes structured prediction in a probabilistic regression framework could also be attempted by alternative GPbased models including Twin GP [4], Dependent Output GP [5], and Sparse Convolved GP =-=[1]-=- for multi-output regression. However... MA ...
In particular, we explore the use of the Wasserstein GAN (WGAN) as a multi-output regression method. The resulting method we call Multi-Output Regression GANs (MOR-GANs) and its performance is compared to a Gaussian Process Regression method (GPR)鈥攁 commonly used non-parametric ...
The method employs Gaussian-process regression to cons... M Drohmann,K Carlberg - arXiv 被引量: 34发表: 2014年 Sliced Latin hypercube designs via orthogonal arrays Such designs are particularly suitable for computer experiments with both qualitative and quantitative factors, multi-fidelity computer ...
and kernel ridge regression (k.r.r.). Our approach outperforms the other methods, particularly for the case of very few basis functions, i.e. a very high sparsity ratio. 展开 会议名称: Machine Learning, Proceedings of the Twenty-Fifth International Conference (ICML 2008), Helsinki, Finland,...
gaussian_process.GaussianProcessClassifier(setting multi_class = “one_vs_rest”) svm.LinearSVC(setting multi_class=”ovr”) linear_model.LogisticRegression(setting multi_class=”ovr”) linear_model.LogisticRegressionCV(setting multi_class=”ovr”) ...
Multi-output Gaussian process In the case of a single fidelity GP, training data takes the form of a matrix of material representations X and corresponding property values \(\vec y\), and we have another matrix of representations X* for which we would like to make predictions. We suppose ...
The study in [19] proposed a model called Variational Heteroscedastic Gaussian Process Regression (VHGPR). This model is designed with the aim of delivering robust and broadly applicable predictions for wind speed, showcasing high-performance characteristics. The CEEMDAN technique is employed for ...
Kriging is a Gaussian process interpolation method used to develop approximations from sample data. This surrogate modelling technique is effective in approximating the Pareto frontier in multi-objective optimisation problems (Couckuyt et al., 2014). It comprises a regression termfx, which explains the...
The dot product in Equation (13) can be replaced by a kernel function when the nonlinear regression is considered. Finally, the output response of the SVR model can be obtained through: 𝑓̂(𝐱)=∑𝑖=1𝑙(𝛼𝑖−𝛼∗𝑖)𝑘(𝐱𝐢·𝐱)+𝑏f^(x)=∑i=1lαi−α...