论文标题
通过专家模型的混合物，Lebesgue空间中条件概率密度函数的近似值
Approximations of conditional probability density functions in Lebesgue spaces via mixture of experts models
论文作者
论文摘要
专家（MOE）模型的混合物被广泛应用于有条件的概率密度估计问题。当输入和输出变量都紧凑时，我们通过证明密度的密度来证明MOE模型类别的丰富性。当输入是单变量时，我们进一步证明了几乎均匀的收敛性。证明了辅助引理有关软马克斯门控函数类别的丰富性及其与高斯门控函数类别的关系。
Mixture of experts (MoE) models are widely applied for conditional probability density estimation problems. We demonstrate the richness of the class of MoE models by proving denseness results in Lebesgue spaces, when inputs and outputs variables are both compactly supported. We further prove an almost uniform convergence result when the input is univariate. Auxiliary lemmas are proved regarding the richness of the soft-max gating function class, and their relationships to the class of Gaussian gating functions.
