Gaussian Process Regression Models Gaussian process regression (GPR) models are nonparametric kernel-based probabilistic models. You can train a GPR model using the fitrgp function. Consider the training set {(xi,yi);i=1,2,...,n}, where xi∈ℝd and yi∈ℝ, drawn from an unknown ...
高斯随机回归在统计上是参数模型 (parametric models)。 首先,参数模型和非参数模型的区别在于模型中的参数量是否随数据的增大而增大,或者说模型的参数是有限个还是无限个(finite or infinite).线性模型的参数仅在于线性部分,而GPR的参数在于线性部分和kernel部分,并且kernel部分是模型的重点; 线性模型的计算复杂度是O(...
Structure discovery in nonparametric regression through compositional kernel search., in: ICML (3), 2013, pp. 1166–1174. [6] Z. Chen, B. Wang, How priors of initial hyperparameters affect Gaussian process regression models. Neurocomputing(2016): 275. [7] F. Bachoc, Cross Validation and M...
Gaussian process regression models (kriging) Gaussian process regression (GPR) models are nonparametric, kernel-based probabilistic models. To train a GPR model interactively, use theRegression Learnerapp. For greater flexibility, train a GPR model using thefitrgpfunction at the command line. After tra...
Gaussian Process Regression Models for the Prediction of Hydrogen Bond Acceptor Strengths.hydrogen bondsstructure-property relationmachine learningcomputational chemistrydensity functional theoryWe present two approaches for the computation of hydrogen bond acceptor strengths, one by machine‐learning and one ...
Concurrent regression modelsCovariance kernelExponential familyNonparametric regressionIn this article, we propose a generalized Gaussian process concurrent regression model for functional data, where the functional response variable has a binomial, Poisson, or other non-Gaussian distribution from an exponential...
How priors of initial hyperparameters affect Gaussian process regression models 不过，好处的是速度快，...
R2022b: A cross-validated Gaussian process regression model is a RegressionPartitionedGP object See Also RegressionGP | predict | compact Topics Gaussian Process Regression Models Kernel (Covariance) Function Options Introduction to Feature SelectionWhy...
this article proposes a new self-adaptive Gaussian process regression model by using multiple kernel function. On the one hand, this proposed method can fit the predicted function in a more large RKHS, and adapts to solve the selection problem of kernel function. On the other hand, this method...
[2] Chen, Zexun.Gaussian process regression methods and extensions for stock market prediction. Diss. Department of Mathematics, 2017. [3] Chen, Zexun, and Bo Wang. "How priors of initial hyperparameters affect Gaussian process regression models."arXiv preprint arXiv:1605.07906(2016)....