基于高斯过程分类的应用近年来受到越来越多的关注。另外，高斯过程分类与一

The application of classification based on Gaussian process has received more and more attention in recent years. <br>In addition, the Gaussian process classification is combined with some classic algorithms to form some practically valuable deformation algorithms, such as the Gaussian process with constraints. Again, the Gaussian process is a model based on nuclear technology. Non-linear learning is an important challenge in the process of machine learning model and algorithm design. Kernel trick, as a powerful tool to realize model nonlinear learning, has been widely used in machine learning, pattern recognition and other fields in recent years. As a special covariance function, the kernel function can be used to construct (or directly replace) the covariance function in the Gaussian process, so as to realize the nonlinear learning of the Gaussian process model. At the same time, in the process of model optimization, the Gaussian process model can optimize the hyperparameters contained in the kernel function by maximizing the marginal likelihood, which effectively reduces the computational cost of the kernel function parameter selection.

The application of Gauss process classification has received more and more attention in recent years.<br>In addition, Gauss process classification is combined with some classical algorithms to form some useful deformation algorithms, such as the constrained Gauss process. Third, the Gauss process is a model based on nuclear technology. Nonlinear learning is an important challenge in the design of machine learning models and algorithms. Kernel trick, as a powerful tool for implementing nonlinear learning of models, has been widely used in machine learning, pattern recognition and other fields in recent years. As a special covariance function, the nuclear function can be used to build (or directly replace) the covariance function in gaussy process, so as to realize the nonlinear learning of Gauss process model. At the same time, in the process of model optimization, Gauss process model can optimize the superparametrics contained in the nuclear function by maximizing the marginal likelihood, effectively reducing the computational cost of the choice of nuclear function parameters.

In recent years, the application of Gaussian process classification has attracted more and more attention.<br>In addition, Gaussian process classification is combined with some classical algorithms to form some practical deformation algorithms, such as constrained Gaussian process. Thirdly, Gaussian process is a model based on kernel technology. Nonlinear learning is an important challenge in the design of machine learning models and algorithms. As a powerful tool to realize model nonlinear learning, kernel trick has been widely used in machine learning, pattern recognition and other fields in recent years. As a special covariance function, kernel function can be used to construct (or directly replace) covariance function in Gaussian process, so as to realize nonlinear learning of Gaussian process model. At the same time, in the process of model optimization, Gaussian process model can optimize the super parameters contained in the kernel function by maximizing the marginal likelihood, which can effectively reduce the computational cost of kernel function parameter selection.<br>