Functional data analysisFunctional covariatesMultivariate responseSemi-metricsGaussian process regression (GPR) has been shown to be a powerful and effective nonparametric method for regression, classification
In this paper, we propose a unified framework which is used not only to introduce a novel multivariate Student -t -t process regression model (MV-TPR) for multi-output prediction, but also to reformulate the multivariate Gaussian process regression (MV-GPR) that overcomes some limitations of ...
(6) is mathematically equivalent to the projection pursuit regression model. The net defined in Eq. (6) as well as the one illustrated in Fig. 20 has just one hidden layer with p and p = 2 nodes, respectively. These nets may be generalized to accommodate more than one hidden layer and...
Hybrid Gaussian process regression with temporal feature extraction for partially interpretable remaining useful life interval prediction in Aeroengine prognostics ArticleOpen access01 April 2025 Background & Summary Resistance spot welding (RSW) is one of the most popular welding procedures for low-carbon...
Utilizing undisturbed soil sampling approach to predict elastic modulus of cohesive soils: a Gaussian process regression model Article 27 May 2024 Introduction The shear strength of soils is a crucial input parameter in almost all civil engineering applications in the soil environment. Soil shear stren...
Joint segmentation of multivariate time series with hidden process regression for human activity recognition. Neurocomputing 120, 633–644; 10.1016/j.neucom.2013.04.003 (2013). Article Google Scholar Chamroukhi, F. Piecewise regression mixture for simultaneous functional data clustering and optimal ...
where, for j = 1,2,3,εjtis Gaussian with mean 0 and covariance matrix Σj=j[1−0.1−0.11]. Create Fully Specified Model Create the Markov-switching dynamic regression model that describesytandst. Get % Switching mechanismP = [10 1 1; 1 10 1; 1 1 10]; ...
Initialize the learnable parameters for the embed operation using the initializeGaussian function attached to the example as a supporting file. To access the function, open the example as a live script. Get sz = [embeddingDimension inputSize]; mu = 0; sigma = 0.01; parameters.e...
regression. We show that conditional superlevel sets have favorable mathematical and intuitive features, and support a clear probabilistic interpretation. We derive the superlevel sets for a conditional or marginal density of interest from an (overfitted) multivariateGaussianmixture model. This approach ...
Gaussian process regression GPC: Gaussian process classifier MVPA: Multivariate pattern analysis GLM: General linear model SVM: Support vector machine BD: Bipolar disorder sMRI: Structural magnetic resonance imaging rs-fMRI: Resting-state functional magnetic resonance imaging ...