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。
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。
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.
应用推荐