The structure of the GGM is explored by the connection between the local Markov property of texture features and the conditional regression of Gaussian random variables.
根据纹理特征的局部马尔可夫性和高斯变量的条件回归之间的关系，将复杂的模型选择转变为较简单的变量选择，应用惩罚正则化技巧同步选择邻域和估计参数。
Kernal methods and support Vector Machines (SVMs) are related to Gaussian processes and can also be used in classification and regression problems.
内核方法和支持向量机(SVMs)与高斯过程相关并应用于分类和回归问题。
Kernal methods and support Vector Machines (SVMs) are related to Gaussian processes and can also be used in classification and regression problems.
内核方法和支持向量机(SVMs)与高斯过程相关并应用于分类和回归问题。
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