针对高维数据下贝叶斯网络结构学习精度和效率低的问题,提出一种基于归一化互信息和近似马尔可夫毯的特征选择(feature selection based on normalized mutual information and approximate Markov blanket,FSNMB)算法来获取目标节点的...针对高维数据下贝叶斯网络结构学习精度和效率低的问题,提出一种基于归一化互信息和近似马尔可夫毯的特征选择(feature selection based on normalized mutual information and approximate Markov blanket,FSNMB)算法来获取目标节点的马尔可夫毯(Markov blanket,MB),进一步结合MB和Meek规则实现基于特征选择的局部贝叶斯网络结构(construct local Bayesian network based on feature selection,FSCLBN)算法,提高局部贝叶斯网络结构学习的精度和效率。实验证明,在高维数据中,FSCLBN算法与现存的局部贝叶斯网络结构学习算法相比更具优势。展开更多
The extension of the solar lower overshooting zone is discussed. There is no any near discontinuity of the derivatives of sound speed at the bottom of the lower overshooting zone, as described by the phenomenological ...The extension of the solar lower overshooting zone is discussed. There is no any near discontinuity of the derivatives of sound speed at the bottom of the lower overshooting zone, as described by the phenomenological non local mixing length theory. On the contrary, the temperature gradient turns smoothly from nearly adiabatic stratification into radiative one. In the lower overshooting zone the temperature gradient is sub adiabatic, but it is super radiative! Namely rad << ad . The extension of the super radiative region (defined by L r/L ⊙≥1.01) is around 0.63 H p (0.053 R ⊙ ).展开更多
文摘针对高维数据下贝叶斯网络结构学习精度和效率低的问题,提出一种基于归一化互信息和近似马尔可夫毯的特征选择(feature selection based on normalized mutual information and approximate Markov blanket,FSNMB)算法来获取目标节点的马尔可夫毯(Markov blanket,MB),进一步结合MB和Meek规则实现基于特征选择的局部贝叶斯网络结构(construct local Bayesian network based on feature selection,FSCLBN)算法,提高局部贝叶斯网络结构学习的精度和效率。实验证明,在高维数据中,FSCLBN算法与现存的局部贝叶斯网络结构学习算法相比更具优势。
文摘The extension of the solar lower overshooting zone is discussed. There is no any near discontinuity of the derivatives of sound speed at the bottom of the lower overshooting zone, as described by the phenomenological non local mixing length theory. On the contrary, the temperature gradient turns smoothly from nearly adiabatic stratification into radiative one. In the lower overshooting zone the temperature gradient is sub adiabatic, but it is super radiative! Namely rad << ad . The extension of the super radiative region (defined by L r/L ⊙≥1.01) is around 0.63 H p (0.053 R ⊙ ).
